Copyright	(c) 2013-2021 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay <brendan.g.hay+amazonka@gmail.com>
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	None

Amazonka.MachineLearning

Contents

Service Configuration
Errors
Waiters
Operations
Types

Description

Derived from API version 2014-12-12 of the AWS service descriptions, licensed under Apache 2.0.

Definition of the public APIs exposed by Amazon Machine Learning

Synopsis

defaultService :: Service
_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError
_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError
_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError
_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError
_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError
_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError
_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError
_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError
newMLModelAvailable :: Wait DescribeMLModels
newBatchPredictionAvailable :: Wait DescribeBatchPredictions
newDataSourceAvailable :: Wait DescribeDataSources
newEvaluationAvailable :: Wait DescribeEvaluations
data UpdateDataSource = UpdateDataSource' Text Text
newUpdateDataSource :: Text -> Text -> UpdateDataSource
data UpdateDataSourceResponse = UpdateDataSourceResponse' (Maybe Text) Int
newUpdateDataSourceResponse :: Int -> UpdateDataSourceResponse
data DeleteDataSource = DeleteDataSource' Text
newDeleteDataSource :: Text -> DeleteDataSource
data DeleteDataSourceResponse = DeleteDataSourceResponse' (Maybe Text) Int
newDeleteDataSourceResponse :: Int -> DeleteDataSourceResponse
data DescribeTags = DescribeTags' Text TaggableResourceType
newDescribeTags :: Text -> TaggableResourceType -> DescribeTags
data DescribeTagsResponse = DescribeTagsResponse' (Maybe Text) (Maybe TaggableResourceType) (Maybe [Tag]) Int
newDescribeTagsResponse :: Int -> DescribeTagsResponse
data CreateDataSourceFromRedshift = CreateDataSourceFromRedshift' (Maybe Text) (Maybe Bool) Text RedshiftDataSpec Text
newCreateDataSourceFromRedshift :: Text -> RedshiftDataSpec -> Text -> CreateDataSourceFromRedshift
data CreateDataSourceFromRedshiftResponse = CreateDataSourceFromRedshiftResponse' (Maybe Text) Int
newCreateDataSourceFromRedshiftResponse :: Int -> CreateDataSourceFromRedshiftResponse
data CreateDataSourceFromS3 = CreateDataSourceFromS3' (Maybe Text) (Maybe Bool) Text S3DataSpec
newCreateDataSourceFromS3 :: Text -> S3DataSpec -> CreateDataSourceFromS3
data CreateDataSourceFromS3Response = CreateDataSourceFromS3Response' (Maybe Text) Int
newCreateDataSourceFromS3Response :: Int -> CreateDataSourceFromS3Response
data CreateMLModel = CreateMLModel' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe (HashMap Text Text)) Text MLModelType Text
newCreateMLModel :: Text -> MLModelType -> Text -> CreateMLModel
data CreateMLModelResponse = CreateMLModelResponse' (Maybe Text) Int
newCreateMLModelResponse :: Int -> CreateMLModelResponse
data DeleteTags = DeleteTags' [Text] Text TaggableResourceType
newDeleteTags :: Text -> TaggableResourceType -> DeleteTags
data DeleteTagsResponse = DeleteTagsResponse' (Maybe Text) (Maybe TaggableResourceType) Int
newDeleteTagsResponse :: Int -> DeleteTagsResponse
data DeleteBatchPrediction = DeleteBatchPrediction' Text
newDeleteBatchPrediction :: Text -> DeleteBatchPrediction
data DeleteBatchPredictionResponse = DeleteBatchPredictionResponse' (Maybe Text) Int
newDeleteBatchPredictionResponse :: Int -> DeleteBatchPredictionResponse
data UpdateBatchPrediction = UpdateBatchPrediction' Text Text
newUpdateBatchPrediction :: Text -> Text -> UpdateBatchPrediction
data UpdateBatchPredictionResponse = UpdateBatchPredictionResponse' (Maybe Text) Int
newUpdateBatchPredictionResponse :: Int -> UpdateBatchPredictionResponse
data GetMLModel = GetMLModel' (Maybe Bool) Text
newGetMLModel :: Text -> GetMLModel
data GetMLModelResponse = GetMLModelResponse' (Maybe EntityStatus) (Maybe POSIX) (Maybe (HashMap Text Text)) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe Text) (Maybe POSIX) (Maybe Double) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe RealtimeEndpointInfo) (Maybe Text) (Maybe Text) (Maybe MLModelType) Int
newGetMLModelResponse :: Int -> GetMLModelResponse
data GetDataSource = GetDataSource' (Maybe Bool) Text
newGetDataSource :: Text -> GetDataSource
data GetDataSourceResponse = GetDataSourceResponse' (Maybe EntityStatus) (Maybe Integer) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe RDSMetadata) (Maybe Integer) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Bool) (Maybe Text) (Maybe RedshiftMetadata) (Maybe Text) (Maybe Text) Int
newGetDataSourceResponse :: Int -> GetDataSourceResponse
data UpdateEvaluation = UpdateEvaluation' Text Text
newUpdateEvaluation :: Text -> Text -> UpdateEvaluation
data UpdateEvaluationResponse = UpdateEvaluationResponse' (Maybe Text) Int
newUpdateEvaluationResponse :: Int -> UpdateEvaluationResponse
data DeleteEvaluation = DeleteEvaluation' Text
newDeleteEvaluation :: Text -> DeleteEvaluation
data DeleteEvaluationResponse = DeleteEvaluationResponse' (Maybe Text) Int
newDeleteEvaluationResponse :: Int -> DeleteEvaluationResponse
data DeleteMLModel = DeleteMLModel' Text
newDeleteMLModel :: Text -> DeleteMLModel
data DeleteMLModelResponse = DeleteMLModelResponse' (Maybe Text) Int
newDeleteMLModelResponse :: Int -> DeleteMLModelResponse
data UpdateMLModel = UpdateMLModel' (Maybe Text) (Maybe Double) Text
newUpdateMLModel :: Text -> UpdateMLModel
data UpdateMLModelResponse = UpdateMLModelResponse' (Maybe Text) Int
newUpdateMLModelResponse :: Int -> UpdateMLModelResponse
data GetBatchPrediction = GetBatchPrediction' Text
newGetBatchPrediction :: Text -> GetBatchPrediction
data GetBatchPredictionResponse = GetBatchPredictionResponse' (Maybe EntityStatus) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) Int
newGetBatchPredictionResponse :: Int -> GetBatchPredictionResponse
data DescribeBatchPredictions = DescribeBatchPredictions' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) (Maybe Natural) (Maybe Text) (Maybe BatchPredictionFilterVariable) (Maybe Text)
newDescribeBatchPredictions :: DescribeBatchPredictions
data DescribeBatchPredictionsResponse = DescribeBatchPredictionsResponse' (Maybe [BatchPrediction]) (Maybe Text) Int
newDescribeBatchPredictionsResponse :: Int -> DescribeBatchPredictionsResponse
data CreateDataSourceFromRDS = CreateDataSourceFromRDS' (Maybe Text) (Maybe Bool) Text RDSDataSpec Text
newCreateDataSourceFromRDS :: Text -> RDSDataSpec -> Text -> CreateDataSourceFromRDS
data CreateDataSourceFromRDSResponse = CreateDataSourceFromRDSResponse' (Maybe Text) Int
newCreateDataSourceFromRDSResponse :: Int -> CreateDataSourceFromRDSResponse
data CreateEvaluation = CreateEvaluation' (Maybe Text) Text Text Text
newCreateEvaluation :: Text -> Text -> Text -> CreateEvaluation
data CreateEvaluationResponse = CreateEvaluationResponse' (Maybe Text) Int
newCreateEvaluationResponse :: Int -> CreateEvaluationResponse
data Predict = Predict' Text (HashMap Text Text) Text
newPredict :: Text -> Text -> Predict
data PredictResponse = PredictResponse' (Maybe Prediction) Int
newPredictResponse :: Int -> PredictResponse
data DeleteRealtimeEndpoint = DeleteRealtimeEndpoint' Text
newDeleteRealtimeEndpoint :: Text -> DeleteRealtimeEndpoint
data DeleteRealtimeEndpointResponse = DeleteRealtimeEndpointResponse' (Maybe RealtimeEndpointInfo) (Maybe Text) Int
newDeleteRealtimeEndpointResponse :: Int -> DeleteRealtimeEndpointResponse
data CreateBatchPrediction = CreateBatchPrediction' (Maybe Text) Text Text Text Text
newCreateBatchPrediction :: Text -> Text -> Text -> Text -> CreateBatchPrediction
data CreateBatchPredictionResponse = CreateBatchPredictionResponse' (Maybe Text) Int
newCreateBatchPredictionResponse :: Int -> CreateBatchPredictionResponse
data GetEvaluation = GetEvaluation' Text
newGetEvaluation :: Text -> GetEvaluation
data GetEvaluationResponse = GetEvaluationResponse' (Maybe EntityStatus) (Maybe PerformanceMetrics) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) Int
newGetEvaluationResponse :: Int -> GetEvaluationResponse
data DescribeEvaluations = DescribeEvaluations' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) (Maybe Natural) (Maybe Text) (Maybe EvaluationFilterVariable) (Maybe Text)
newDescribeEvaluations :: DescribeEvaluations
data DescribeEvaluationsResponse = DescribeEvaluationsResponse' (Maybe [Evaluation]) (Maybe Text) Int
newDescribeEvaluationsResponse :: Int -> DescribeEvaluationsResponse
data CreateRealtimeEndpoint = CreateRealtimeEndpoint' Text
newCreateRealtimeEndpoint :: Text -> CreateRealtimeEndpoint
data CreateRealtimeEndpointResponse = CreateRealtimeEndpointResponse' (Maybe RealtimeEndpointInfo) (Maybe Text) Int
newCreateRealtimeEndpointResponse :: Int -> CreateRealtimeEndpointResponse
data AddTags = AddTags' [Tag] Text TaggableResourceType
newAddTags :: Text -> TaggableResourceType -> AddTags
data AddTagsResponse = AddTagsResponse' (Maybe Text) (Maybe TaggableResourceType) Int
newAddTagsResponse :: Int -> AddTagsResponse
data DescribeMLModels = DescribeMLModels' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) (Maybe Natural) (Maybe Text) (Maybe MLModelFilterVariable) (Maybe Text)
newDescribeMLModels :: DescribeMLModels
data DescribeMLModelsResponse = DescribeMLModelsResponse' (Maybe [MLModel]) (Maybe Text) Int
newDescribeMLModelsResponse :: Int -> DescribeMLModelsResponse
data DescribeDataSources = DescribeDataSources' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) (Maybe Natural) (Maybe Text) (Maybe DataSourceFilterVariable) (Maybe Text)
newDescribeDataSources :: DescribeDataSources
data DescribeDataSourcesResponse = DescribeDataSourcesResponse' (Maybe [DataSource]) (Maybe Text) Int
newDescribeDataSourcesResponse :: Int -> DescribeDataSourcesResponse
newtype Algorithm where
- Algorithm' {
  - fromAlgorithm :: Text
  }
- pattern Algorithm_Sgd :: Algorithm
newtype BatchPredictionFilterVariable where
- BatchPredictionFilterVariable' {
  - fromBatchPredictionFilterVariable :: Text
  }
- pattern BatchPredictionFilterVariable_CreatedAt :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_DataSourceId :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_DataURI :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_IAMUser :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_LastUpdatedAt :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_MLModelId :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_Name :: BatchPredictionFilterVariable
- pattern BatchPredictionFilterVariable_Status :: BatchPredictionFilterVariable
newtype DataSourceFilterVariable where
- DataSourceFilterVariable' {
  - fromDataSourceFilterVariable :: Text
  }
- pattern DataSourceFilterVariable_CreatedAt :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_DataLocationS3 :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_IAMUser :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_LastUpdatedAt :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_Name :: DataSourceFilterVariable
- pattern DataSourceFilterVariable_Status :: DataSourceFilterVariable
newtype DetailsAttributes where
- DetailsAttributes' {
  - fromDetailsAttributes :: Text
  }
- pattern DetailsAttributes_Algorithm :: DetailsAttributes
- pattern DetailsAttributes_PredictiveModelType :: DetailsAttributes
newtype EntityStatus where
- EntityStatus' {
  - fromEntityStatus :: Text
  }
- pattern EntityStatus_COMPLETED :: EntityStatus
- pattern EntityStatus_DELETED :: EntityStatus
- pattern EntityStatus_FAILED :: EntityStatus
- pattern EntityStatus_INPROGRESS :: EntityStatus
- pattern EntityStatus_PENDING :: EntityStatus
newtype EvaluationFilterVariable where
- EvaluationFilterVariable' {
  - fromEvaluationFilterVariable :: Text
  }
- pattern EvaluationFilterVariable_CreatedAt :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_DataSourceId :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_DataURI :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_IAMUser :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_LastUpdatedAt :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_MLModelId :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_Name :: EvaluationFilterVariable
- pattern EvaluationFilterVariable_Status :: EvaluationFilterVariable
newtype MLModelFilterVariable where
- MLModelFilterVariable' {
  - fromMLModelFilterVariable :: Text
  }
- pattern MLModelFilterVariable_Algorithm :: MLModelFilterVariable
- pattern MLModelFilterVariable_CreatedAt :: MLModelFilterVariable
- pattern MLModelFilterVariable_IAMUser :: MLModelFilterVariable
- pattern MLModelFilterVariable_LastUpdatedAt :: MLModelFilterVariable
- pattern MLModelFilterVariable_MLModelType :: MLModelFilterVariable
- pattern MLModelFilterVariable_Name :: MLModelFilterVariable
- pattern MLModelFilterVariable_RealtimeEndpointStatus :: MLModelFilterVariable
- pattern MLModelFilterVariable_Status :: MLModelFilterVariable
- pattern MLModelFilterVariable_TrainingDataSourceId :: MLModelFilterVariable
- pattern MLModelFilterVariable_TrainingDataURI :: MLModelFilterVariable
newtype MLModelType where
- MLModelType' {
  - fromMLModelType :: Text
  }
- pattern MLModelType_BINARY :: MLModelType
- pattern MLModelType_MULTICLASS :: MLModelType
- pattern MLModelType_REGRESSION :: MLModelType
newtype RealtimeEndpointStatus where
- RealtimeEndpointStatus' {
  - fromRealtimeEndpointStatus :: Text
  }
- pattern RealtimeEndpointStatus_FAILED :: RealtimeEndpointStatus
- pattern RealtimeEndpointStatus_NONE :: RealtimeEndpointStatus
- pattern RealtimeEndpointStatus_READY :: RealtimeEndpointStatus
- pattern RealtimeEndpointStatus_UPDATING :: RealtimeEndpointStatus
newtype SortOrder where
- SortOrder' {
  - fromSortOrder :: Text
  }
- pattern SortOrder_Asc :: SortOrder
- pattern SortOrder_Dsc :: SortOrder
newtype TaggableResourceType where
- TaggableResourceType' {
  - fromTaggableResourceType :: Text
  }
- pattern TaggableResourceType_BatchPrediction :: TaggableResourceType
- pattern TaggableResourceType_DataSource :: TaggableResourceType
- pattern TaggableResourceType_Evaluation :: TaggableResourceType
- pattern TaggableResourceType_MLModel :: TaggableResourceType
data BatchPrediction = BatchPrediction' (Maybe EntityStatus) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text)
newBatchPrediction :: BatchPrediction
data DataSource = DataSource' (Maybe EntityStatus) (Maybe Integer) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe RDSMetadata) (Maybe Integer) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Bool) (Maybe Text) (Maybe RedshiftMetadata) (Maybe Text) (Maybe Text)
newDataSource :: DataSource
data Evaluation = Evaluation' (Maybe EntityStatus) (Maybe PerformanceMetrics) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text)
newEvaluation :: Evaluation
data MLModel = MLModel' (Maybe EntityStatus) (Maybe POSIX) (Maybe (HashMap Text Text)) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Double) (Maybe POSIX) (Maybe Algorithm) (Maybe Text) (Maybe Text) (Maybe RealtimeEndpointInfo) (Maybe Text) (Maybe Text) (Maybe MLModelType)
newMLModel :: MLModel
data PerformanceMetrics = PerformanceMetrics' (Maybe (HashMap Text Text))
newPerformanceMetrics :: PerformanceMetrics
data Prediction = Prediction' (Maybe Double) (Maybe Text) (Maybe (HashMap Text Double)) (Maybe (HashMap DetailsAttributes Text))
newPrediction :: Prediction
data RDSDataSpec = RDSDataSpec' (Maybe Text) (Maybe Text) (Maybe Text) RDSDatabase Text RDSDatabaseCredentials Text Text Text Text [Text]
newRDSDataSpec :: RDSDatabase -> Text -> RDSDatabaseCredentials -> Text -> Text -> Text -> Text -> RDSDataSpec
data RDSDatabase = RDSDatabase' Text Text
newRDSDatabase :: Text -> Text -> RDSDatabase
data RDSDatabaseCredentials = RDSDatabaseCredentials' Text Text
newRDSDatabaseCredentials :: Text -> Text -> RDSDatabaseCredentials
data RDSMetadata = RDSMetadata' (Maybe Text) (Maybe Text) (Maybe RDSDatabase) (Maybe Text) (Maybe Text) (Maybe Text)
newRDSMetadata :: RDSMetadata
data RealtimeEndpointInfo = RealtimeEndpointInfo' (Maybe POSIX) (Maybe Text) (Maybe RealtimeEndpointStatus) (Maybe Int)
newRealtimeEndpointInfo :: RealtimeEndpointInfo
data RedshiftDataSpec = RedshiftDataSpec' (Maybe Text) (Maybe Text) (Maybe Text) RedshiftDatabase Text RedshiftDatabaseCredentials Text
newRedshiftDataSpec :: RedshiftDatabase -> Text -> RedshiftDatabaseCredentials -> Text -> RedshiftDataSpec
data RedshiftDatabase = RedshiftDatabase' Text Text
newRedshiftDatabase :: Text -> Text -> RedshiftDatabase
data RedshiftDatabaseCredentials = RedshiftDatabaseCredentials' Text Text
newRedshiftDatabaseCredentials :: Text -> Text -> RedshiftDatabaseCredentials
data RedshiftMetadata = RedshiftMetadata' (Maybe Text) (Maybe RedshiftDatabase) (Maybe Text)
newRedshiftMetadata :: RedshiftMetadata
data S3DataSpec = S3DataSpec' (Maybe Text) (Maybe Text) (Maybe Text) Text
newS3DataSpec :: Text -> S3DataSpec
data Tag = Tag' (Maybe Text) (Maybe Text)
newTag :: Tag

Service Configuration

defaultService :: Service Source #

API version 2014-12-12 of the Amazon Machine Learning SDK configuration.

Errors

Error matchers are designed for use with the functions provided by Control.Exception.Lens. This allows catching (and rethrowing) service specific errors returned by MachineLearning.

InvalidTagException

_InvalidTagException :: AsError a => Getting (First ServiceError) a ServiceError Source #

Prism for InvalidTagException' errors.

InternalServerException

_InternalServerException :: AsError a => Getting (First ServiceError) a ServiceError Source #

An error on the server occurred when trying to process a request.

InvalidInputException

_InvalidInputException :: AsError a => Getting (First ServiceError) a ServiceError Source #

An error on the client occurred. Typically, the cause is an invalid input value.

IdempotentParameterMismatchException

_IdempotentParameterMismatchException :: AsError a => Getting (First ServiceError) a ServiceError Source #

A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.

TagLimitExceededException

_TagLimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError Source #

Prism for TagLimitExceededException' errors.

PredictorNotMountedException

_PredictorNotMountedException :: AsError a => Getting (First ServiceError) a ServiceError Source #

The exception is thrown when a predict request is made to an unmounted MLModel.

ResourceNotFoundException

_ResourceNotFoundException :: AsError a => Getting (First ServiceError) a ServiceError Source #

A specified resource cannot be located.

LimitExceededException

_LimitExceededException :: AsError a => Getting (First ServiceError) a ServiceError Source #

The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.

Waiters

Waiters poll by repeatedly sending a request until some remote success condition configured by the Wait specification is fulfilled. The Wait specification determines how many attempts should be made, in addition to delay and retry strategies.

MLModelAvailable

newMLModelAvailable :: Wait DescribeMLModels Source #

Polls DescribeMLModels every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

BatchPredictionAvailable

newBatchPredictionAvailable :: Wait DescribeBatchPredictions Source #

Polls DescribeBatchPredictions every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

DataSourceAvailable

newDataSourceAvailable :: Wait DescribeDataSources Source #

Polls DescribeDataSources every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

EvaluationAvailable

newEvaluationAvailable :: Wait DescribeEvaluations Source #

Polls DescribeEvaluations every 30 seconds until a successful state is reached. An error is returned after 60 failed checks.

Operations

Some AWS operations return results that are incomplete and require subsequent requests in order to obtain the entire result set. The process of sending subsequent requests to continue where a previous request left off is called pagination. For example, the ListObjects operation of Amazon S3 returns up to 1000 objects at a time, and you must send subsequent requests with the appropriate Marker in order to retrieve the next page of results.

Operations that have an AWSPager instance can transparently perform subsequent requests, correctly setting Markers and other request facets to iterate through the entire result set of a truncated API operation. Operations which support this have an additional note in the documentation.

Many operations have the ability to filter results on the server side. See the individual operation parameters for details.

UpdateDataSource

data UpdateDataSource Source #

See: newUpdateDataSource smart constructor.

Constructors

UpdateDataSource' Text Text

Instances

Instances details

Eq UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods (==) :: UpdateDataSource -> UpdateDataSource -> Bool # (/=) :: UpdateDataSource -> UpdateDataSource -> Bool #
Read UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods readsPrec :: Int -> ReadS UpdateDataSource # readList :: ReadS [UpdateDataSource] # readPrec :: ReadPrec UpdateDataSource # readListPrec :: ReadPrec [UpdateDataSource] #
Show UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods showsPrec :: Int -> UpdateDataSource -> ShowS # show :: UpdateDataSource -> String # showList :: [UpdateDataSource] -> ShowS #
Generic UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Associated Types type Rep UpdateDataSource :: Type -> Type # Methods from :: UpdateDataSource -> Rep UpdateDataSource x # to :: Rep UpdateDataSource x -> UpdateDataSource #
NFData UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods rnf :: UpdateDataSource -> () #
Hashable UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods hashWithSalt :: Int -> UpdateDataSource -> Int # hash :: UpdateDataSource -> Int #
ToJSON UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods toJSON :: UpdateDataSource -> Value # toEncoding :: UpdateDataSource -> Encoding # toJSONList :: [UpdateDataSource] -> Value # toEncodingList :: [UpdateDataSource] -> Encoding #
AWSRequest UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Associated Types type AWSResponse UpdateDataSource # Methods request :: UpdateDataSource -> Request UpdateDataSource # response :: MonadResource m => Logger -> Service -> Proxy UpdateDataSource -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse UpdateDataSource))) #
ToHeaders UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods toHeaders :: UpdateDataSource -> [Header] #
ToPath UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods toPath :: UpdateDataSource -> ByteString #
ToQuery UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods toQuery :: UpdateDataSource -> QueryString #
type Rep UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource type Rep UpdateDataSource = D1 ('MetaData "UpdateDataSource" "Amazonka.MachineLearning.UpdateDataSource" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "UpdateDataSource'" 'PrefixI 'True) (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "dataSourceName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse UpdateDataSource Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource type AWSResponse UpdateDataSource = UpdateDataSourceResponse

newUpdateDataSource Source #

Arguments

:: Text	`$sel:dataSourceId:UpdateDataSource'`
-> Text	`$sel:dataSourceName:UpdateDataSource'`
-> UpdateDataSource

Create a value of UpdateDataSource with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceId:UpdateDataSource', updateDataSource_dataSourceId - The ID assigned to the DataSource during creation.

$sel:dataSourceName:UpdateDataSource', updateDataSource_dataSourceName - A new user-supplied name or description of the DataSource that will replace the current description.

data UpdateDataSourceResponse Source #

Represents the output of an UpdateDataSource operation.

You can see the updated content by using the GetBatchPrediction operation.

See: newUpdateDataSourceResponse smart constructor.

Constructors

UpdateDataSourceResponse' (Maybe Text) Int

Instances

Instances details

Eq UpdateDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods (==) :: UpdateDataSourceResponse -> UpdateDataSourceResponse -> Bool # (/=) :: UpdateDataSourceResponse -> UpdateDataSourceResponse -> Bool #
Read UpdateDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods readsPrec :: Int -> ReadS UpdateDataSourceResponse # readList :: ReadS [UpdateDataSourceResponse] # readPrec :: ReadPrec UpdateDataSourceResponse # readListPrec :: ReadPrec [UpdateDataSourceResponse] #
Show UpdateDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods showsPrec :: Int -> UpdateDataSourceResponse -> ShowS # show :: UpdateDataSourceResponse -> String # showList :: [UpdateDataSourceResponse] -> ShowS #
Generic UpdateDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Associated Types type Rep UpdateDataSourceResponse :: Type -> Type # Methods from :: UpdateDataSourceResponse -> Rep UpdateDataSourceResponse x # to :: Rep UpdateDataSourceResponse x -> UpdateDataSourceResponse #
NFData UpdateDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource Methods rnf :: UpdateDataSourceResponse -> () #
type Rep UpdateDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateDataSource type Rep UpdateDataSourceResponse = D1 ('MetaData "UpdateDataSourceResponse" "Amazonka.MachineLearning.UpdateDataSource" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "UpdateDataSourceResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newUpdateDataSourceResponse Source #

Arguments

:: Int	`$sel:httpStatus:UpdateDataSourceResponse'`
-> UpdateDataSourceResponse

Create a value of UpdateDataSourceResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceId:UpdateDataSource', updateDataSourceResponse_dataSourceId - The ID assigned to the DataSource during creation. This value should be identical to the value of the DataSourceID in the request.

$sel:httpStatus:UpdateDataSourceResponse', updateDataSourceResponse_httpStatus - The response's http status code.

DeleteDataSource

data DeleteDataSource Source #

See: newDeleteDataSource smart constructor.

Constructors

DeleteDataSource' Text

Instances

Instances details

Eq DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods (==) :: DeleteDataSource -> DeleteDataSource -> Bool # (/=) :: DeleteDataSource -> DeleteDataSource -> Bool #
Read DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods readsPrec :: Int -> ReadS DeleteDataSource # readList :: ReadS [DeleteDataSource] # readPrec :: ReadPrec DeleteDataSource # readListPrec :: ReadPrec [DeleteDataSource] #
Show DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods showsPrec :: Int -> DeleteDataSource -> ShowS # show :: DeleteDataSource -> String # showList :: [DeleteDataSource] -> ShowS #
Generic DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Associated Types type Rep DeleteDataSource :: Type -> Type # Methods from :: DeleteDataSource -> Rep DeleteDataSource x # to :: Rep DeleteDataSource x -> DeleteDataSource #
NFData DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods rnf :: DeleteDataSource -> () #
Hashable DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods hashWithSalt :: Int -> DeleteDataSource -> Int # hash :: DeleteDataSource -> Int #
ToJSON DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods toJSON :: DeleteDataSource -> Value # toEncoding :: DeleteDataSource -> Encoding # toJSONList :: [DeleteDataSource] -> Value # toEncodingList :: [DeleteDataSource] -> Encoding #
AWSRequest DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Associated Types type AWSResponse DeleteDataSource # Methods request :: DeleteDataSource -> Request DeleteDataSource # response :: MonadResource m => Logger -> Service -> Proxy DeleteDataSource -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DeleteDataSource))) #
ToHeaders DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods toHeaders :: DeleteDataSource -> [Header] #
ToPath DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods toPath :: DeleteDataSource -> ByteString #
ToQuery DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods toQuery :: DeleteDataSource -> QueryString #
type Rep DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource type Rep DeleteDataSource = D1 ('MetaData "DeleteDataSource" "Amazonka.MachineLearning.DeleteDataSource" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteDataSource'" 'PrefixI 'True) (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse DeleteDataSource Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource type AWSResponse DeleteDataSource = DeleteDataSourceResponse

newDeleteDataSource Source #

Arguments

:: Text	`$sel:dataSourceId:DeleteDataSource'`
-> DeleteDataSource

Create a value of DeleteDataSource with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceId:DeleteDataSource', deleteDataSource_dataSourceId - A user-supplied ID that uniquely identifies the DataSource.

data DeleteDataSourceResponse Source #

Represents the output of a DeleteDataSource operation.

See: newDeleteDataSourceResponse smart constructor.

Constructors

DeleteDataSourceResponse' (Maybe Text) Int

Instances

Instances details

Eq DeleteDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods (==) :: DeleteDataSourceResponse -> DeleteDataSourceResponse -> Bool # (/=) :: DeleteDataSourceResponse -> DeleteDataSourceResponse -> Bool #
Read DeleteDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods readsPrec :: Int -> ReadS DeleteDataSourceResponse # readList :: ReadS [DeleteDataSourceResponse] # readPrec :: ReadPrec DeleteDataSourceResponse # readListPrec :: ReadPrec [DeleteDataSourceResponse] #
Show DeleteDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods showsPrec :: Int -> DeleteDataSourceResponse -> ShowS # show :: DeleteDataSourceResponse -> String # showList :: [DeleteDataSourceResponse] -> ShowS #
Generic DeleteDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Associated Types type Rep DeleteDataSourceResponse :: Type -> Type # Methods from :: DeleteDataSourceResponse -> Rep DeleteDataSourceResponse x # to :: Rep DeleteDataSourceResponse x -> DeleteDataSourceResponse #
NFData DeleteDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource Methods rnf :: DeleteDataSourceResponse -> () #
type Rep DeleteDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteDataSource type Rep DeleteDataSourceResponse = D1 ('MetaData "DeleteDataSourceResponse" "Amazonka.MachineLearning.DeleteDataSource" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteDataSourceResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newDeleteDataSourceResponse Source #

Arguments

:: Int	`$sel:httpStatus:DeleteDataSourceResponse'`
-> DeleteDataSourceResponse

Create a value of DeleteDataSourceResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceId:DeleteDataSource', deleteDataSourceResponse_dataSourceId - A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request.

$sel:httpStatus:DeleteDataSourceResponse', deleteDataSourceResponse_httpStatus - The response's http status code.

DescribeTags

data DescribeTags Source #

See: newDescribeTags smart constructor.

Constructors

DescribeTags' Text TaggableResourceType

Instances

Instances details

Eq DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods (==) :: DescribeTags -> DescribeTags -> Bool # (/=) :: DescribeTags -> DescribeTags -> Bool #
Read DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods readsPrec :: Int -> ReadS DescribeTags # readList :: ReadS [DescribeTags] # readPrec :: ReadPrec DescribeTags # readListPrec :: ReadPrec [DescribeTags] #
Show DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods showsPrec :: Int -> DescribeTags -> ShowS # show :: DescribeTags -> String # showList :: [DescribeTags] -> ShowS #
Generic DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Associated Types type Rep DescribeTags :: Type -> Type # Methods from :: DescribeTags -> Rep DescribeTags x # to :: Rep DescribeTags x -> DescribeTags #
NFData DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods rnf :: DescribeTags -> () #
Hashable DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods hashWithSalt :: Int -> DescribeTags -> Int # hash :: DescribeTags -> Int #
ToJSON DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods toJSON :: DescribeTags -> Value # toEncoding :: DescribeTags -> Encoding # toJSONList :: [DescribeTags] -> Value # toEncodingList :: [DescribeTags] -> Encoding #
AWSRequest DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Associated Types type AWSResponse DescribeTags # Methods request :: DescribeTags -> Request DescribeTags # response :: MonadResource m => Logger -> Service -> Proxy DescribeTags -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DescribeTags))) #
ToHeaders DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods toHeaders :: DescribeTags -> [Header] #
ToPath DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods toPath :: DescribeTags -> ByteString #
ToQuery DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods toQuery :: DescribeTags -> QueryString #
type Rep DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags type Rep DescribeTags = D1 ('MetaData "DescribeTags" "Amazonka.MachineLearning.DescribeTags" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeTags'" 'PrefixI 'True) (S1 ('MetaSel ('Just "resourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "resourceType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 TaggableResourceType)))
type AWSResponse DescribeTags Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags type AWSResponse DescribeTags = DescribeTagsResponse

newDescribeTags Source #

Arguments

:: Text	`$sel:resourceId:DescribeTags'`
-> TaggableResourceType	`$sel:resourceType:DescribeTags'`
-> DescribeTags

Create a value of DescribeTags with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:resourceId:DescribeTags', describeTags_resourceId - The ID of the ML object. For example, exampleModelId.

$sel:resourceType:DescribeTags', describeTags_resourceType - The type of the ML object.

data DescribeTagsResponse Source #

Amazon ML returns the following elements.

See: newDescribeTagsResponse smart constructor.

Constructors

DescribeTagsResponse' (Maybe Text) (Maybe TaggableResourceType) (Maybe [Tag]) Int

Instances

Instances details

Eq DescribeTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods (==) :: DescribeTagsResponse -> DescribeTagsResponse -> Bool # (/=) :: DescribeTagsResponse -> DescribeTagsResponse -> Bool #
Read DescribeTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods readsPrec :: Int -> ReadS DescribeTagsResponse # readList :: ReadS [DescribeTagsResponse] # readPrec :: ReadPrec DescribeTagsResponse # readListPrec :: ReadPrec [DescribeTagsResponse] #
Show DescribeTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods showsPrec :: Int -> DescribeTagsResponse -> ShowS # show :: DescribeTagsResponse -> String # showList :: [DescribeTagsResponse] -> ShowS #
Generic DescribeTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Associated Types type Rep DescribeTagsResponse :: Type -> Type # Methods from :: DescribeTagsResponse -> Rep DescribeTagsResponse x # to :: Rep DescribeTagsResponse x -> DescribeTagsResponse #
NFData DescribeTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags Methods rnf :: DescribeTagsResponse -> () #
type Rep DescribeTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeTags type Rep DescribeTagsResponse = D1 ('MetaData "DescribeTagsResponse" "Amazonka.MachineLearning.DescribeTags" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeTagsResponse'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "resourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "resourceType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe TaggableResourceType))) :: (S1 ('MetaSel ('Just "tags") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [Tag])) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newDescribeTagsResponse Source #

Arguments

:: Int	`$sel:httpStatus:DescribeTagsResponse'`
-> DescribeTagsResponse

Create a value of DescribeTagsResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:resourceId:DescribeTags', describeTagsResponse_resourceId - The ID of the tagged ML object.

$sel:resourceType:DescribeTags', describeTagsResponse_resourceType - The type of the tagged ML object.

$sel:tags:DescribeTagsResponse', describeTagsResponse_tags - A list of tags associated with the ML object.

$sel:httpStatus:DescribeTagsResponse', describeTagsResponse_httpStatus - The response's http status code.

CreateDataSourceFromRedshift

data CreateDataSourceFromRedshift Source #

See: newCreateDataSourceFromRedshift smart constructor.

Constructors

CreateDataSourceFromRedshift' (Maybe Text) (Maybe Bool) Text RedshiftDataSpec Text

Instances

Instances details

Eq CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods (==) :: CreateDataSourceFromRedshift -> CreateDataSourceFromRedshift -> Bool # (/=) :: CreateDataSourceFromRedshift -> CreateDataSourceFromRedshift -> Bool #
Read CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods readsPrec :: Int -> ReadS CreateDataSourceFromRedshift # readList :: ReadS [CreateDataSourceFromRedshift] # readPrec :: ReadPrec CreateDataSourceFromRedshift # readListPrec :: ReadPrec [CreateDataSourceFromRedshift] #
Show CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods showsPrec :: Int -> CreateDataSourceFromRedshift -> ShowS # show :: CreateDataSourceFromRedshift -> String # showList :: [CreateDataSourceFromRedshift] -> ShowS #
Generic CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Associated Types type Rep CreateDataSourceFromRedshift :: Type -> Type # Methods from :: CreateDataSourceFromRedshift -> Rep CreateDataSourceFromRedshift x # to :: Rep CreateDataSourceFromRedshift x -> CreateDataSourceFromRedshift #
NFData CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods rnf :: CreateDataSourceFromRedshift -> () #
Hashable CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods hashWithSalt :: Int -> CreateDataSourceFromRedshift -> Int # hash :: CreateDataSourceFromRedshift -> Int #
ToJSON CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods toJSON :: CreateDataSourceFromRedshift -> Value # toEncoding :: CreateDataSourceFromRedshift -> Encoding # toJSONList :: [CreateDataSourceFromRedshift] -> Value # toEncodingList :: [CreateDataSourceFromRedshift] -> Encoding #
AWSRequest CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Associated Types type AWSResponse CreateDataSourceFromRedshift # Methods request :: CreateDataSourceFromRedshift -> Request CreateDataSourceFromRedshift # response :: MonadResource m => Logger -> Service -> Proxy CreateDataSourceFromRedshift -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateDataSourceFromRedshift))) #
ToHeaders CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods toHeaders :: CreateDataSourceFromRedshift -> [Header] #
ToPath CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods toPath :: CreateDataSourceFromRedshift -> ByteString #
ToQuery CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods toQuery :: CreateDataSourceFromRedshift -> QueryString #
type Rep CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift type Rep CreateDataSourceFromRedshift = D1 ('MetaData "CreateDataSourceFromRedshift" "Amazonka.MachineLearning.CreateDataSourceFromRedshift" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateDataSourceFromRedshift'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "dataSourceName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "computeStatistics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool))) :: (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: (S1 ('MetaSel ('Just "dataSpec") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 RedshiftDataSpec) :: S1 ('MetaSel ('Just "roleARN") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))))
type AWSResponse CreateDataSourceFromRedshift Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift type AWSResponse CreateDataSourceFromRedshift = CreateDataSourceFromRedshiftResponse

newCreateDataSourceFromRedshift Source #

Arguments

:: Text	`$sel:dataSourceId:CreateDataSourceFromRedshift'`
-> RedshiftDataSpec	`$sel:dataSpec:CreateDataSourceFromRedshift'`
-> Text	`$sel:roleARN:CreateDataSourceFromRedshift'`
-> CreateDataSourceFromRedshift

Create a value of CreateDataSourceFromRedshift with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceName:CreateDataSourceFromRedshift', createDataSourceFromRedshift_dataSourceName - A user-supplied name or description of the DataSource.

$sel:computeStatistics:CreateDataSourceFromRedshift', createDataSourceFromRedshift_computeStatistics - The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the DataSource needs to be used for MLModel training.

$sel:dataSourceId:CreateDataSourceFromRedshift', createDataSourceFromRedshift_dataSourceId - A user-supplied ID that uniquely identifies the DataSource.

$sel:dataSpec:CreateDataSourceFromRedshift', createDataSourceFromRedshift_dataSpec - The data specification of an Amazon Redshift DataSource:

DatabaseInformation -
- DatabaseName - The name of the Amazon Redshift database.
- ClusterIdentifier - The unique ID for the Amazon Redshift cluster.
DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.
SelectSqlQuery - The query that is used to retrieve the observation data for the Datasource.
S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery query is stored in this location.
DataSchemaUri - The Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.
DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the DataSource.
Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

$sel:roleARN:CreateDataSourceFromRedshift', createDataSourceFromRedshift_roleARN - A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:

A security group to allow Amazon ML to execute the SelectSqlQuery query on an Amazon Redshift cluster
An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation

data CreateDataSourceFromRedshiftResponse Source #

Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

See: newCreateDataSourceFromRedshiftResponse smart constructor.

Constructors

CreateDataSourceFromRedshiftResponse' (Maybe Text) Int

Instances

Instances details

Eq CreateDataSourceFromRedshiftResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods (==) :: CreateDataSourceFromRedshiftResponse -> CreateDataSourceFromRedshiftResponse -> Bool # (/=) :: CreateDataSourceFromRedshiftResponse -> CreateDataSourceFromRedshiftResponse -> Bool #
Read CreateDataSourceFromRedshiftResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods readsPrec :: Int -> ReadS CreateDataSourceFromRedshiftResponse # readList :: ReadS [CreateDataSourceFromRedshiftResponse] # readPrec :: ReadPrec CreateDataSourceFromRedshiftResponse # readListPrec :: ReadPrec [CreateDataSourceFromRedshiftResponse] #
Show CreateDataSourceFromRedshiftResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods showsPrec :: Int -> CreateDataSourceFromRedshiftResponse -> ShowS # show :: CreateDataSourceFromRedshiftResponse -> String # showList :: [CreateDataSourceFromRedshiftResponse] -> ShowS #
Generic CreateDataSourceFromRedshiftResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Associated Types type Rep CreateDataSourceFromRedshiftResponse :: Type -> Type # Methods from :: CreateDataSourceFromRedshiftResponse -> Rep CreateDataSourceFromRedshiftResponse x # to :: Rep CreateDataSourceFromRedshiftResponse x -> CreateDataSourceFromRedshiftResponse #
NFData CreateDataSourceFromRedshiftResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift Methods rnf :: CreateDataSourceFromRedshiftResponse -> () #
type Rep CreateDataSourceFromRedshiftResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRedshift type Rep CreateDataSourceFromRedshiftResponse = D1 ('MetaData "CreateDataSourceFromRedshiftResponse" "Amazonka.MachineLearning.CreateDataSourceFromRedshift" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateDataSourceFromRedshiftResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newCreateDataSourceFromRedshiftResponse Source #

Arguments

:: Int	`$sel:httpStatus:CreateDataSourceFromRedshiftResponse'`
-> CreateDataSourceFromRedshiftResponse

Create a value of CreateDataSourceFromRedshiftResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceId:CreateDataSourceFromRedshift', createDataSourceFromRedshiftResponse_dataSourceId - A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request.

$sel:httpStatus:CreateDataSourceFromRedshiftResponse', createDataSourceFromRedshiftResponse_httpStatus - The response's http status code.

CreateDataSourceFromS3

data CreateDataSourceFromS3 Source #

See: newCreateDataSourceFromS3 smart constructor.

Constructors

CreateDataSourceFromS3' (Maybe Text) (Maybe Bool) Text S3DataSpec

Instances

Instances details

Eq CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods (==) :: CreateDataSourceFromS3 -> CreateDataSourceFromS3 -> Bool # (/=) :: CreateDataSourceFromS3 -> CreateDataSourceFromS3 -> Bool #
Read CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods readsPrec :: Int -> ReadS CreateDataSourceFromS3 # readList :: ReadS [CreateDataSourceFromS3] # readPrec :: ReadPrec CreateDataSourceFromS3 # readListPrec :: ReadPrec [CreateDataSourceFromS3] #
Show CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods showsPrec :: Int -> CreateDataSourceFromS3 -> ShowS # show :: CreateDataSourceFromS3 -> String # showList :: [CreateDataSourceFromS3] -> ShowS #
Generic CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Associated Types type Rep CreateDataSourceFromS3 :: Type -> Type # Methods from :: CreateDataSourceFromS3 -> Rep CreateDataSourceFromS3 x # to :: Rep CreateDataSourceFromS3 x -> CreateDataSourceFromS3 #
NFData CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods rnf :: CreateDataSourceFromS3 -> () #
Hashable CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods hashWithSalt :: Int -> CreateDataSourceFromS3 -> Int # hash :: CreateDataSourceFromS3 -> Int #
ToJSON CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods toJSON :: CreateDataSourceFromS3 -> Value # toEncoding :: CreateDataSourceFromS3 -> Encoding # toJSONList :: [CreateDataSourceFromS3] -> Value # toEncodingList :: [CreateDataSourceFromS3] -> Encoding #
AWSRequest CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Associated Types type AWSResponse CreateDataSourceFromS3 # Methods request :: CreateDataSourceFromS3 -> Request CreateDataSourceFromS3 # response :: MonadResource m => Logger -> Service -> Proxy CreateDataSourceFromS3 -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateDataSourceFromS3))) #
ToHeaders CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods toHeaders :: CreateDataSourceFromS3 -> [Header] #
ToPath CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods toPath :: CreateDataSourceFromS3 -> ByteString #
ToQuery CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods toQuery :: CreateDataSourceFromS3 -> QueryString #
type Rep CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 type Rep CreateDataSourceFromS3 = D1 ('MetaData "CreateDataSourceFromS3" "Amazonka.MachineLearning.CreateDataSourceFromS3" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateDataSourceFromS3'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "dataSourceName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "computeStatistics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool))) :: (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "dataSpec") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 S3DataSpec))))
type AWSResponse CreateDataSourceFromS3 Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 type AWSResponse CreateDataSourceFromS3 = CreateDataSourceFromS3Response

newCreateDataSourceFromS3 Source #

Arguments

:: Text	`$sel:dataSourceId:CreateDataSourceFromS3'`
-> S3DataSpec	`$sel:dataSpec:CreateDataSourceFromS3'`
-> CreateDataSourceFromS3

Create a value of CreateDataSourceFromS3 with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceName:CreateDataSourceFromS3', createDataSourceFromS3_dataSourceName - A user-supplied name or description of the DataSource.

$sel:computeStatistics:CreateDataSourceFromS3', createDataSourceFromS3_computeStatistics - The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the @DataSource needs to be used for MLModel@ training.

$sel:dataSourceId:CreateDataSourceFromS3', createDataSourceFromS3_dataSourceId - A user-supplied identifier that uniquely identifies the DataSource.

$sel:dataSpec:CreateDataSourceFromS3', createDataSourceFromS3_dataSpec - The data specification of a DataSource:

DataLocationS3 - The Amazon S3 location of the observation data.
DataSchemaLocationS3 - The Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.
DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource.
Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

data CreateDataSourceFromS3Response Source #

Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter.

See: newCreateDataSourceFromS3Response smart constructor.

Constructors

CreateDataSourceFromS3Response' (Maybe Text) Int

Instances

Instances details

Eq CreateDataSourceFromS3Response Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods (==) :: CreateDataSourceFromS3Response -> CreateDataSourceFromS3Response -> Bool # (/=) :: CreateDataSourceFromS3Response -> CreateDataSourceFromS3Response -> Bool #
Read CreateDataSourceFromS3Response Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods readsPrec :: Int -> ReadS CreateDataSourceFromS3Response # readList :: ReadS [CreateDataSourceFromS3Response] # readPrec :: ReadPrec CreateDataSourceFromS3Response # readListPrec :: ReadPrec [CreateDataSourceFromS3Response] #
Show CreateDataSourceFromS3Response Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods showsPrec :: Int -> CreateDataSourceFromS3Response -> ShowS # show :: CreateDataSourceFromS3Response -> String # showList :: [CreateDataSourceFromS3Response] -> ShowS #
Generic CreateDataSourceFromS3Response Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Associated Types type Rep CreateDataSourceFromS3Response :: Type -> Type # Methods from :: CreateDataSourceFromS3Response -> Rep CreateDataSourceFromS3Response x # to :: Rep CreateDataSourceFromS3Response x -> CreateDataSourceFromS3Response #
NFData CreateDataSourceFromS3Response Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 Methods rnf :: CreateDataSourceFromS3Response -> () #
type Rep CreateDataSourceFromS3Response Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromS3 type Rep CreateDataSourceFromS3Response = D1 ('MetaData "CreateDataSourceFromS3Response" "Amazonka.MachineLearning.CreateDataSourceFromS3" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateDataSourceFromS3Response'" 'PrefixI 'True) (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newCreateDataSourceFromS3Response Source #

Arguments

:: Int	`$sel:httpStatus:CreateDataSourceFromS3Response'`
-> CreateDataSourceFromS3Response

Create a value of CreateDataSourceFromS3Response with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceId:CreateDataSourceFromS3', createDataSourceFromS3Response_dataSourceId - A user-supplied ID that uniquely identifies the DataSource. This value should be identical to the value of the DataSourceID in the request.

$sel:httpStatus:CreateDataSourceFromS3Response', createDataSourceFromS3Response_httpStatus - The response's http status code.

CreateMLModel

data CreateMLModel Source #

See: newCreateMLModel smart constructor.

Constructors

CreateMLModel' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe (HashMap Text Text)) Text MLModelType Text

Instances

Instances details

Eq CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods (==) :: CreateMLModel -> CreateMLModel -> Bool # (/=) :: CreateMLModel -> CreateMLModel -> Bool #
Read CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods readsPrec :: Int -> ReadS CreateMLModel # readList :: ReadS [CreateMLModel] # readPrec :: ReadPrec CreateMLModel # readListPrec :: ReadPrec [CreateMLModel] #
Show CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods showsPrec :: Int -> CreateMLModel -> ShowS # show :: CreateMLModel -> String # showList :: [CreateMLModel] -> ShowS #
Generic CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Associated Types type Rep CreateMLModel :: Type -> Type # Methods from :: CreateMLModel -> Rep CreateMLModel x # to :: Rep CreateMLModel x -> CreateMLModel #
NFData CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods rnf :: CreateMLModel -> () #
Hashable CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods hashWithSalt :: Int -> CreateMLModel -> Int # hash :: CreateMLModel -> Int #
ToJSON CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods toJSON :: CreateMLModel -> Value # toEncoding :: CreateMLModel -> Encoding # toJSONList :: [CreateMLModel] -> Value # toEncodingList :: [CreateMLModel] -> Encoding #
AWSRequest CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Associated Types type AWSResponse CreateMLModel # Methods request :: CreateMLModel -> Request CreateMLModel # response :: MonadResource m => Logger -> Service -> Proxy CreateMLModel -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateMLModel))) #
ToHeaders CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods toHeaders :: CreateMLModel -> [Header] #
ToPath CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods toPath :: CreateMLModel -> ByteString #
ToQuery CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods toQuery :: CreateMLModel -> QueryString #
type Rep CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel type Rep CreateMLModel = D1 ('MetaData "CreateMLModel" "Amazonka.MachineLearning.CreateMLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateMLModel'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "recipe") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "recipeUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "mLModelName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :: ((S1 ('MetaSel ('Just "parameters") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text))) :: S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)) :: (S1 ('MetaSel ('Just "mLModelType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 MLModelType) :: S1 ('MetaSel ('Just "trainingDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))))
type AWSResponse CreateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel type AWSResponse CreateMLModel = CreateMLModelResponse

newCreateMLModel Source #

Arguments

:: Text	`$sel:mLModelId:CreateMLModel'`
-> MLModelType	`$sel:mLModelType:CreateMLModel'`
-> Text	`$sel:trainingDataSourceId:CreateMLModel'`
-> CreateMLModel

Create a value of CreateMLModel with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:recipe:CreateMLModel', createMLModel_recipe - The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

$sel:recipeUri:CreateMLModel', createMLModel_recipeUri - The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

$sel:mLModelName:CreateMLModel', createMLModel_mLModelName - A user-supplied name or description of the MLModel.

$sel:parameters:CreateMLModel', createMLModel_parameters - A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.
sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

$sel:mLModelId:CreateMLModel', createMLModel_mLModelId - A user-supplied ID that uniquely identifies the MLModel.

$sel:mLModelType:CreateMLModel', createMLModel_mLModelType - The category of supervised learning that this MLModel will address. Choose from the following types:

Choose REGRESSION if the MLModel will be used to predict a numeric value.
Choose BINARY if the MLModel result has two possible values.
Choose MULTICLASS if the MLModel result has a limited number of values.

For more information, see the Amazon Machine Learning Developer Guide.

$sel:trainingDataSourceId:CreateMLModel', createMLModel_trainingDataSourceId - The DataSource that points to the training data.

data CreateMLModelResponse Source #

Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.

The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status parameter.

See: newCreateMLModelResponse smart constructor.

Constructors

CreateMLModelResponse' (Maybe Text) Int

Instances

Instances details

Eq CreateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods (==) :: CreateMLModelResponse -> CreateMLModelResponse -> Bool # (/=) :: CreateMLModelResponse -> CreateMLModelResponse -> Bool #
Read CreateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods readsPrec :: Int -> ReadS CreateMLModelResponse # readList :: ReadS [CreateMLModelResponse] # readPrec :: ReadPrec CreateMLModelResponse # readListPrec :: ReadPrec [CreateMLModelResponse] #
Show CreateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods showsPrec :: Int -> CreateMLModelResponse -> ShowS # show :: CreateMLModelResponse -> String # showList :: [CreateMLModelResponse] -> ShowS #
Generic CreateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Associated Types type Rep CreateMLModelResponse :: Type -> Type # Methods from :: CreateMLModelResponse -> Rep CreateMLModelResponse x # to :: Rep CreateMLModelResponse x -> CreateMLModelResponse #
NFData CreateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel Methods rnf :: CreateMLModelResponse -> () #
type Rep CreateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateMLModel type Rep CreateMLModelResponse = D1 ('MetaData "CreateMLModelResponse" "Amazonka.MachineLearning.CreateMLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateMLModelResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newCreateMLModelResponse Source #

Arguments

:: Int	`$sel:httpStatus:CreateMLModelResponse'`
-> CreateMLModelResponse

Create a value of CreateMLModelResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelId:CreateMLModel', createMLModelResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

$sel:httpStatus:CreateMLModelResponse', createMLModelResponse_httpStatus - The response's http status code.

DeleteTags

data DeleteTags Source #

See: newDeleteTags smart constructor.

Constructors

DeleteTags' [Text] Text TaggableResourceType

Instances

Instances details

Eq DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods (==) :: DeleteTags -> DeleteTags -> Bool # (/=) :: DeleteTags -> DeleteTags -> Bool #
Read DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods readsPrec :: Int -> ReadS DeleteTags # readList :: ReadS [DeleteTags] # readPrec :: ReadPrec DeleteTags # readListPrec :: ReadPrec [DeleteTags] #
Show DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods showsPrec :: Int -> DeleteTags -> ShowS # show :: DeleteTags -> String # showList :: [DeleteTags] -> ShowS #
Generic DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Associated Types type Rep DeleteTags :: Type -> Type # Methods from :: DeleteTags -> Rep DeleteTags x # to :: Rep DeleteTags x -> DeleteTags #
NFData DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods rnf :: DeleteTags -> () #
Hashable DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods hashWithSalt :: Int -> DeleteTags -> Int # hash :: DeleteTags -> Int #
ToJSON DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods toJSON :: DeleteTags -> Value # toEncoding :: DeleteTags -> Encoding # toJSONList :: [DeleteTags] -> Value # toEncodingList :: [DeleteTags] -> Encoding #
AWSRequest DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Associated Types type AWSResponse DeleteTags # Methods request :: DeleteTags -> Request DeleteTags # response :: MonadResource m => Logger -> Service -> Proxy DeleteTags -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DeleteTags))) #
ToHeaders DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods toHeaders :: DeleteTags -> [Header] #
ToPath DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods toPath :: DeleteTags -> ByteString #
ToQuery DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods toQuery :: DeleteTags -> QueryString #
type Rep DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags type Rep DeleteTags = D1 ('MetaData "DeleteTags" "Amazonka.MachineLearning.DeleteTags" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteTags'" 'PrefixI 'True) (S1 ('MetaSel ('Just "tagKeys") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 [Text]) :: (S1 ('MetaSel ('Just "resourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: S1 ('MetaSel ('Just "resourceType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 TaggableResourceType))))
type AWSResponse DeleteTags Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags type AWSResponse DeleteTags = DeleteTagsResponse

newDeleteTags Source #

Arguments

:: Text	`$sel:resourceId:DeleteTags'`
-> TaggableResourceType	`$sel:resourceType:DeleteTags'`
-> DeleteTags

Create a value of DeleteTags with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:tagKeys:DeleteTags', deleteTags_tagKeys - One or more tags to delete.

$sel:resourceId:DeleteTags', deleteTags_resourceId - The ID of the tagged ML object. For example, exampleModelId.

$sel:resourceType:DeleteTags', deleteTags_resourceType - The type of the tagged ML object.

data DeleteTagsResponse Source #

Amazon ML returns the following elements.

See: newDeleteTagsResponse smart constructor.

Constructors

DeleteTagsResponse' (Maybe Text) (Maybe TaggableResourceType) Int

Instances

Instances details

Eq DeleteTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods (==) :: DeleteTagsResponse -> DeleteTagsResponse -> Bool # (/=) :: DeleteTagsResponse -> DeleteTagsResponse -> Bool #
Read DeleteTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods readsPrec :: Int -> ReadS DeleteTagsResponse # readList :: ReadS [DeleteTagsResponse] # readPrec :: ReadPrec DeleteTagsResponse # readListPrec :: ReadPrec [DeleteTagsResponse] #
Show DeleteTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods showsPrec :: Int -> DeleteTagsResponse -> ShowS # show :: DeleteTagsResponse -> String # showList :: [DeleteTagsResponse] -> ShowS #
Generic DeleteTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Associated Types type Rep DeleteTagsResponse :: Type -> Type # Methods from :: DeleteTagsResponse -> Rep DeleteTagsResponse x # to :: Rep DeleteTagsResponse x -> DeleteTagsResponse #
NFData DeleteTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags Methods rnf :: DeleteTagsResponse -> () #
type Rep DeleteTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteTags type Rep DeleteTagsResponse = D1 ('MetaData "DeleteTagsResponse" "Amazonka.MachineLearning.DeleteTags" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteTagsResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "resourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "resourceType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe TaggableResourceType)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newDeleteTagsResponse Source #

Arguments

:: Int	`$sel:httpStatus:DeleteTagsResponse'`
-> DeleteTagsResponse

Create a value of DeleteTagsResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:resourceId:DeleteTags', deleteTagsResponse_resourceId - The ID of the ML object from which tags were deleted.

$sel:resourceType:DeleteTags', deleteTagsResponse_resourceType - The type of the ML object from which tags were deleted.

$sel:httpStatus:DeleteTagsResponse', deleteTagsResponse_httpStatus - The response's http status code.

DeleteBatchPrediction

data DeleteBatchPrediction Source #

See: newDeleteBatchPrediction smart constructor.

Constructors

DeleteBatchPrediction' Text

Instances

Instances details

Eq DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods (==) :: DeleteBatchPrediction -> DeleteBatchPrediction -> Bool # (/=) :: DeleteBatchPrediction -> DeleteBatchPrediction -> Bool #
Read DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods readsPrec :: Int -> ReadS DeleteBatchPrediction # readList :: ReadS [DeleteBatchPrediction] # readPrec :: ReadPrec DeleteBatchPrediction # readListPrec :: ReadPrec [DeleteBatchPrediction] #
Show DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods showsPrec :: Int -> DeleteBatchPrediction -> ShowS # show :: DeleteBatchPrediction -> String # showList :: [DeleteBatchPrediction] -> ShowS #
Generic DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Associated Types type Rep DeleteBatchPrediction :: Type -> Type # Methods from :: DeleteBatchPrediction -> Rep DeleteBatchPrediction x # to :: Rep DeleteBatchPrediction x -> DeleteBatchPrediction #
NFData DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods rnf :: DeleteBatchPrediction -> () #
Hashable DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods hashWithSalt :: Int -> DeleteBatchPrediction -> Int # hash :: DeleteBatchPrediction -> Int #
ToJSON DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods toJSON :: DeleteBatchPrediction -> Value # toEncoding :: DeleteBatchPrediction -> Encoding # toJSONList :: [DeleteBatchPrediction] -> Value # toEncodingList :: [DeleteBatchPrediction] -> Encoding #
AWSRequest DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Associated Types type AWSResponse DeleteBatchPrediction # Methods request :: DeleteBatchPrediction -> Request DeleteBatchPrediction # response :: MonadResource m => Logger -> Service -> Proxy DeleteBatchPrediction -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DeleteBatchPrediction))) #
ToHeaders DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods toHeaders :: DeleteBatchPrediction -> [Header] #
ToPath DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods toPath :: DeleteBatchPrediction -> ByteString #
ToQuery DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods toQuery :: DeleteBatchPrediction -> QueryString #
type Rep DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction type Rep DeleteBatchPrediction = D1 ('MetaData "DeleteBatchPrediction" "Amazonka.MachineLearning.DeleteBatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteBatchPrediction'" 'PrefixI 'True) (S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse DeleteBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction type AWSResponse DeleteBatchPrediction = DeleteBatchPredictionResponse

newDeleteBatchPrediction Source #

Arguments

:: Text	`$sel:batchPredictionId:DeleteBatchPrediction'`
-> DeleteBatchPrediction

Create a value of DeleteBatchPrediction with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:batchPredictionId:DeleteBatchPrediction', deleteBatchPrediction_batchPredictionId - A user-supplied ID that uniquely identifies the BatchPrediction.

data DeleteBatchPredictionResponse Source #

Represents the output of a DeleteBatchPrediction operation.

You can use the GetBatchPrediction operation and check the value of the Status parameter to see whether a BatchPrediction is marked as DELETED.

See: newDeleteBatchPredictionResponse smart constructor.

Constructors

DeleteBatchPredictionResponse' (Maybe Text) Int

Instances

Instances details

Eq DeleteBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods (==) :: DeleteBatchPredictionResponse -> DeleteBatchPredictionResponse -> Bool # (/=) :: DeleteBatchPredictionResponse -> DeleteBatchPredictionResponse -> Bool #
Read DeleteBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods readsPrec :: Int -> ReadS DeleteBatchPredictionResponse # readList :: ReadS [DeleteBatchPredictionResponse] # readPrec :: ReadPrec DeleteBatchPredictionResponse # readListPrec :: ReadPrec [DeleteBatchPredictionResponse] #
Show DeleteBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods showsPrec :: Int -> DeleteBatchPredictionResponse -> ShowS # show :: DeleteBatchPredictionResponse -> String # showList :: [DeleteBatchPredictionResponse] -> ShowS #
Generic DeleteBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Associated Types type Rep DeleteBatchPredictionResponse :: Type -> Type # Methods from :: DeleteBatchPredictionResponse -> Rep DeleteBatchPredictionResponse x # to :: Rep DeleteBatchPredictionResponse x -> DeleteBatchPredictionResponse #
NFData DeleteBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction Methods rnf :: DeleteBatchPredictionResponse -> () #
type Rep DeleteBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteBatchPrediction type Rep DeleteBatchPredictionResponse = D1 ('MetaData "DeleteBatchPredictionResponse" "Amazonka.MachineLearning.DeleteBatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteBatchPredictionResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newDeleteBatchPredictionResponse Source #

Arguments

:: Int	`$sel:httpStatus:DeleteBatchPredictionResponse'`
-> DeleteBatchPredictionResponse

Create a value of DeleteBatchPredictionResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:batchPredictionId:DeleteBatchPrediction', deleteBatchPredictionResponse_batchPredictionId - A user-supplied ID that uniquely identifies the BatchPrediction. This value should be identical to the value of the BatchPredictionID in the request.

$sel:httpStatus:DeleteBatchPredictionResponse', deleteBatchPredictionResponse_httpStatus - The response's http status code.

UpdateBatchPrediction

data UpdateBatchPrediction Source #

See: newUpdateBatchPrediction smart constructor.

Constructors

UpdateBatchPrediction' Text Text

Instances

Instances details

Eq UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods (==) :: UpdateBatchPrediction -> UpdateBatchPrediction -> Bool # (/=) :: UpdateBatchPrediction -> UpdateBatchPrediction -> Bool #
Read UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods readsPrec :: Int -> ReadS UpdateBatchPrediction # readList :: ReadS [UpdateBatchPrediction] # readPrec :: ReadPrec UpdateBatchPrediction # readListPrec :: ReadPrec [UpdateBatchPrediction] #
Show UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods showsPrec :: Int -> UpdateBatchPrediction -> ShowS # show :: UpdateBatchPrediction -> String # showList :: [UpdateBatchPrediction] -> ShowS #
Generic UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Associated Types type Rep UpdateBatchPrediction :: Type -> Type # Methods from :: UpdateBatchPrediction -> Rep UpdateBatchPrediction x # to :: Rep UpdateBatchPrediction x -> UpdateBatchPrediction #
NFData UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods rnf :: UpdateBatchPrediction -> () #
Hashable UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods hashWithSalt :: Int -> UpdateBatchPrediction -> Int # hash :: UpdateBatchPrediction -> Int #
ToJSON UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods toJSON :: UpdateBatchPrediction -> Value # toEncoding :: UpdateBatchPrediction -> Encoding # toJSONList :: [UpdateBatchPrediction] -> Value # toEncodingList :: [UpdateBatchPrediction] -> Encoding #
AWSRequest UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Associated Types type AWSResponse UpdateBatchPrediction # Methods request :: UpdateBatchPrediction -> Request UpdateBatchPrediction # response :: MonadResource m => Logger -> Service -> Proxy UpdateBatchPrediction -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse UpdateBatchPrediction))) #
ToHeaders UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods toHeaders :: UpdateBatchPrediction -> [Header] #
ToPath UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods toPath :: UpdateBatchPrediction -> ByteString #
ToQuery UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods toQuery :: UpdateBatchPrediction -> QueryString #
type Rep UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction type Rep UpdateBatchPrediction = D1 ('MetaData "UpdateBatchPrediction" "Amazonka.MachineLearning.UpdateBatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "UpdateBatchPrediction'" 'PrefixI 'True) (S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "batchPredictionName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse UpdateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction type AWSResponse UpdateBatchPrediction = UpdateBatchPredictionResponse

newUpdateBatchPrediction Source #

Arguments

:: Text	`$sel:batchPredictionId:UpdateBatchPrediction'`
-> Text	`$sel:batchPredictionName:UpdateBatchPrediction'`
-> UpdateBatchPrediction

Create a value of UpdateBatchPrediction with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:batchPredictionId:UpdateBatchPrediction', updateBatchPrediction_batchPredictionId - The ID assigned to the BatchPrediction during creation.

$sel:batchPredictionName:UpdateBatchPrediction', updateBatchPrediction_batchPredictionName - A new user-supplied name or description of the BatchPrediction.

data UpdateBatchPredictionResponse Source #

Represents the output of an UpdateBatchPrediction operation.

You can see the updated content by using the GetBatchPrediction operation.

See: newUpdateBatchPredictionResponse smart constructor.

Constructors

UpdateBatchPredictionResponse' (Maybe Text) Int

Instances

Instances details

Eq UpdateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods (==) :: UpdateBatchPredictionResponse -> UpdateBatchPredictionResponse -> Bool # (/=) :: UpdateBatchPredictionResponse -> UpdateBatchPredictionResponse -> Bool #
Read UpdateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods readsPrec :: Int -> ReadS UpdateBatchPredictionResponse # readList :: ReadS [UpdateBatchPredictionResponse] # readPrec :: ReadPrec UpdateBatchPredictionResponse # readListPrec :: ReadPrec [UpdateBatchPredictionResponse] #
Show UpdateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods showsPrec :: Int -> UpdateBatchPredictionResponse -> ShowS # show :: UpdateBatchPredictionResponse -> String # showList :: [UpdateBatchPredictionResponse] -> ShowS #
Generic UpdateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Associated Types type Rep UpdateBatchPredictionResponse :: Type -> Type # Methods from :: UpdateBatchPredictionResponse -> Rep UpdateBatchPredictionResponse x # to :: Rep UpdateBatchPredictionResponse x -> UpdateBatchPredictionResponse #
NFData UpdateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction Methods rnf :: UpdateBatchPredictionResponse -> () #
type Rep UpdateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateBatchPrediction type Rep UpdateBatchPredictionResponse = D1 ('MetaData "UpdateBatchPredictionResponse" "Amazonka.MachineLearning.UpdateBatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "UpdateBatchPredictionResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newUpdateBatchPredictionResponse Source #

Arguments

:: Int	`$sel:httpStatus:UpdateBatchPredictionResponse'`
-> UpdateBatchPredictionResponse

Create a value of UpdateBatchPredictionResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:batchPredictionId:UpdateBatchPrediction', updateBatchPredictionResponse_batchPredictionId - The ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request.

$sel:httpStatus:UpdateBatchPredictionResponse', updateBatchPredictionResponse_httpStatus - The response's http status code.

GetMLModel

data GetMLModel Source #

See: newGetMLModel smart constructor.

Constructors

GetMLModel' (Maybe Bool) Text

Instances

Instances details

Eq GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods (==) :: GetMLModel -> GetMLModel -> Bool # (/=) :: GetMLModel -> GetMLModel -> Bool #
Read GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods readsPrec :: Int -> ReadS GetMLModel # readList :: ReadS [GetMLModel] # readPrec :: ReadPrec GetMLModel # readListPrec :: ReadPrec [GetMLModel] #
Show GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods showsPrec :: Int -> GetMLModel -> ShowS # show :: GetMLModel -> String # showList :: [GetMLModel] -> ShowS #
Generic GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Associated Types type Rep GetMLModel :: Type -> Type # Methods from :: GetMLModel -> Rep GetMLModel x # to :: Rep GetMLModel x -> GetMLModel #
NFData GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods rnf :: GetMLModel -> () #
Hashable GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods hashWithSalt :: Int -> GetMLModel -> Int # hash :: GetMLModel -> Int #
ToJSON GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods toJSON :: GetMLModel -> Value # toEncoding :: GetMLModel -> Encoding # toJSONList :: [GetMLModel] -> Value # toEncodingList :: [GetMLModel] -> Encoding #
AWSRequest GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Associated Types type AWSResponse GetMLModel # Methods request :: GetMLModel -> Request GetMLModel # response :: MonadResource m => Logger -> Service -> Proxy GetMLModel -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse GetMLModel))) #
ToHeaders GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods toHeaders :: GetMLModel -> [Header] #
ToPath GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods toPath :: GetMLModel -> ByteString #
ToQuery GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods toQuery :: GetMLModel -> QueryString #
type Rep GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel type Rep GetMLModel = D1 ('MetaData "GetMLModel" "Amazonka.MachineLearning.GetMLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "GetMLModel'" 'PrefixI 'True) (S1 ('MetaSel ('Just "verbose") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :*: S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse GetMLModel Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel type AWSResponse GetMLModel = GetMLModelResponse

newGetMLModel Source #

Arguments

:: Text	`$sel:mLModelId:GetMLModel'`
-> GetMLModel

Create a value of GetMLModel with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:verbose:GetMLModel', getMLModel_verbose - Specifies whether the GetMLModel operation should return Recipe.

If true, Recipe is returned.

If false, Recipe is not returned.

$sel:mLModelId:GetMLModel', getMLModel_mLModelId - The ID assigned to the MLModel at creation.

data GetMLModelResponse Source #

Represents the output of a GetMLModel operation, and provides detailed information about a MLModel.

See: newGetMLModelResponse smart constructor.

Constructors

GetMLModelResponse' (Maybe EntityStatus) (Maybe POSIX) (Maybe (HashMap Text Text)) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe Text) (Maybe POSIX) (Maybe Double) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe RealtimeEndpointInfo) (Maybe Text) (Maybe Text) (Maybe MLModelType) Int

Instances

Instances details

Eq GetMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods (==) :: GetMLModelResponse -> GetMLModelResponse -> Bool # (/=) :: GetMLModelResponse -> GetMLModelResponse -> Bool #
Read GetMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods readsPrec :: Int -> ReadS GetMLModelResponse # readList :: ReadS [GetMLModelResponse] # readPrec :: ReadPrec GetMLModelResponse # readListPrec :: ReadPrec [GetMLModelResponse] #
Show GetMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods showsPrec :: Int -> GetMLModelResponse -> ShowS # show :: GetMLModelResponse -> String # showList :: [GetMLModelResponse] -> ShowS #
Generic GetMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Associated Types type Rep GetMLModelResponse :: Type -> Type # Methods from :: GetMLModelResponse -> Rep GetMLModelResponse x # to :: Rep GetMLModelResponse x -> GetMLModelResponse #
NFData GetMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel Methods rnf :: GetMLModelResponse -> () #
type Rep GetMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetMLModel type Rep GetMLModelResponse = D1 ('MetaData "GetMLModelResponse" "Amazonka.MachineLearning.GetMLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "GetMLModelResponse'" 'PrefixI 'True) ((((S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EntityStatus)) :: S1 ('MetaSel ('Just "lastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))) :: (S1 ('MetaSel ('Just "trainingParameters") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text))) :: (S1 ('MetaSel ('Just "scoreThresholdLastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))))) :: ((S1 ('MetaSel ('Just "computeTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: (S1 ('MetaSel ('Just "recipe") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "inputDataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :: (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "sizeInBytes") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: S1 ('MetaSel ('Just "schema") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))) :: (((S1 ('MetaSel ('Just "startedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "scoreThreshold") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double))) :: (S1 ('MetaSel ('Just "finishedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: (S1 ('MetaSel ('Just "createdByIamUser") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "name") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))) :: ((S1 ('MetaSel ('Just "logUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "endpointInfo") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RealtimeEndpointInfo)) :: S1 ('MetaSel ('Just "trainingDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :: (S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "mLModelType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe MLModelType)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))))))

newGetMLModelResponse Source #

Arguments

:: Int	`$sel:httpStatus:GetMLModelResponse'`
-> GetMLModelResponse

Create a value of GetMLModelResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:status:GetMLModelResponse', getMLModelResponse_status - The current status of the MLModel. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
INPROGRESS - The request is processing.
FAILED - The request did not run to completion. The ML model isn't usable.
COMPLETED - The request completed successfully.
DELETED - The MLModel is marked as deleted. It isn't usable.

$sel:lastUpdatedAt:GetMLModelResponse', getMLModelResponse_lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.

$sel:trainingParameters:GetMLModelResponse', getMLModelResponse_trainingParameters - A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.
sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

$sel:scoreThresholdLastUpdatedAt:GetMLModelResponse', getMLModelResponse_scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

$sel:createdAt:GetMLModelResponse', getMLModelResponse_createdAt - The time that the MLModel was created. The time is expressed in epoch time.

$sel:computeTime:GetMLModelResponse', getMLModelResponse_computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

$sel:recipe:GetMLModelResponse', getMLModelResponse_recipe - The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

Note: This parameter is provided as part of the verbose format.

$sel:inputDataLocationS3:GetMLModelResponse', getMLModelResponse_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

$sel:mLModelId:GetMLModel', getMLModelResponse_mLModelId - The MLModel ID, which is same as the MLModelId in the request.

$sel:sizeInBytes:GetMLModelResponse', getMLModelResponse_sizeInBytes - Undocumented member.

$sel:schema:GetMLModelResponse', getMLModelResponse_schema - The schema used by all of the data files referenced by the DataSource.

Note: This parameter is provided as part of the verbose format.

$sel:startedAt:GetMLModelResponse', getMLModelResponse_startedAt - The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

$sel:scoreThreshold:GetMLModelResponse', getMLModelResponse_scoreThreshold - The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

$sel:finishedAt:GetMLModelResponse', getMLModelResponse_finishedAt - The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

$sel:createdByIamUser:GetMLModelResponse', getMLModelResponse_createdByIamUser - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

$sel:name:GetMLModelResponse', getMLModelResponse_name - A user-supplied name or description of the MLModel.

$sel:logUri:GetMLModelResponse', getMLModelResponse_logUri - A link to the file that contains logs of the CreateMLModel operation.

$sel:endpointInfo:GetMLModelResponse', getMLModelResponse_endpointInfo - The current endpoint of the MLModel

$sel:trainingDataSourceId:GetMLModelResponse', getMLModelResponse_trainingDataSourceId - The ID of the training DataSource.

$sel:message:GetMLModelResponse', getMLModelResponse_message - A description of the most recent details about accessing the MLModel.

$sel:mLModelType:GetMLModelResponse', getMLModelResponse_mLModelType - Identifies the MLModel category. The following are the available types:

REGRESSION -- Produces a numeric result. For example, "What price should a house be listed at?"
BINARY -- Produces one of two possible results. For example, "Is this an e-commerce website?"
MULTICLASS -- Produces one of several possible results. For example, "Is this a HIGH, LOW or MEDIUM risk trade?"

$sel:httpStatus:GetMLModelResponse', getMLModelResponse_httpStatus - The response's http status code.

GetDataSource

data GetDataSource Source #

See: newGetDataSource smart constructor.

Constructors

GetDataSource' (Maybe Bool) Text

Instances

Instances details

Eq GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods (==) :: GetDataSource -> GetDataSource -> Bool # (/=) :: GetDataSource -> GetDataSource -> Bool #
Read GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods readsPrec :: Int -> ReadS GetDataSource # readList :: ReadS [GetDataSource] # readPrec :: ReadPrec GetDataSource # readListPrec :: ReadPrec [GetDataSource] #
Show GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods showsPrec :: Int -> GetDataSource -> ShowS # show :: GetDataSource -> String # showList :: [GetDataSource] -> ShowS #
Generic GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Associated Types type Rep GetDataSource :: Type -> Type # Methods from :: GetDataSource -> Rep GetDataSource x # to :: Rep GetDataSource x -> GetDataSource #
NFData GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods rnf :: GetDataSource -> () #
Hashable GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods hashWithSalt :: Int -> GetDataSource -> Int # hash :: GetDataSource -> Int #
ToJSON GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods toJSON :: GetDataSource -> Value # toEncoding :: GetDataSource -> Encoding # toJSONList :: [GetDataSource] -> Value # toEncodingList :: [GetDataSource] -> Encoding #
AWSRequest GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Associated Types type AWSResponse GetDataSource # Methods request :: GetDataSource -> Request GetDataSource # response :: MonadResource m => Logger -> Service -> Proxy GetDataSource -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse GetDataSource))) #
ToHeaders GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods toHeaders :: GetDataSource -> [Header] #
ToPath GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods toPath :: GetDataSource -> ByteString #
ToQuery GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods toQuery :: GetDataSource -> QueryString #
type Rep GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource type Rep GetDataSource = D1 ('MetaData "GetDataSource" "Amazonka.MachineLearning.GetDataSource" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "GetDataSource'" 'PrefixI 'True) (S1 ('MetaSel ('Just "verbose") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :*: S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse GetDataSource Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource type AWSResponse GetDataSource = GetDataSourceResponse

newGetDataSource Source #

Arguments

:: Text	`$sel:dataSourceId:GetDataSource'`
-> GetDataSource

Create a value of GetDataSource with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:verbose:GetDataSource', getDataSource_verbose - Specifies whether the GetDataSource operation should return DataSourceSchema.

If true, DataSourceSchema is returned.

If false, DataSourceSchema is not returned.

$sel:dataSourceId:GetDataSource', getDataSource_dataSourceId - The ID assigned to the DataSource at creation.

data GetDataSourceResponse Source #

Represents the output of a GetDataSource operation and describes a DataSource.

See: newGetDataSourceResponse smart constructor.

Constructors

GetDataSourceResponse' (Maybe EntityStatus) (Maybe Integer) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe RDSMetadata) (Maybe Integer) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Bool) (Maybe Text) (Maybe RedshiftMetadata) (Maybe Text) (Maybe Text) Int

Instances

Instances details

Eq GetDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods (==) :: GetDataSourceResponse -> GetDataSourceResponse -> Bool # (/=) :: GetDataSourceResponse -> GetDataSourceResponse -> Bool #
Read GetDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods readsPrec :: Int -> ReadS GetDataSourceResponse # readList :: ReadS [GetDataSourceResponse] # readPrec :: ReadPrec GetDataSourceResponse # readListPrec :: ReadPrec [GetDataSourceResponse] #
Show GetDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods showsPrec :: Int -> GetDataSourceResponse -> ShowS # show :: GetDataSourceResponse -> String # showList :: [GetDataSourceResponse] -> ShowS #
Generic GetDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Associated Types type Rep GetDataSourceResponse :: Type -> Type # Methods from :: GetDataSourceResponse -> Rep GetDataSourceResponse x # to :: Rep GetDataSourceResponse x -> GetDataSourceResponse #
NFData GetDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource Methods rnf :: GetDataSourceResponse -> () #
type Rep GetDataSourceResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetDataSource type Rep GetDataSourceResponse = D1 ('MetaData "GetDataSourceResponse" "Amazonka.MachineLearning.GetDataSource" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "GetDataSourceResponse'" 'PrefixI 'True) ((((S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EntityStatus)) :: S1 ('MetaSel ('Just "numberOfFiles") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer))) :: (S1 ('MetaSel ('Just "lastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: (S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "computeTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer))))) :: ((S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "rDSMetadata") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RDSMetadata))) :: (S1 ('MetaSel ('Just "dataSizeInBytes") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: (S1 ('MetaSel ('Just "dataSourceSchema") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "startedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)))))) :: (((S1 ('MetaSel ('Just "finishedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "createdByIamUser") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "name") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "logUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "dataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))) :: ((S1 ('MetaSel ('Just "computeStatistics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :: (S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "redshiftMetadata") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RedshiftMetadata)))) :: (S1 ('MetaSel ('Just "dataRearrangement") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "roleARN") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))))))

newGetDataSourceResponse Source #

Arguments

:: Int	`$sel:httpStatus:GetDataSourceResponse'`
-> GetDataSourceResponse

Create a value of GetDataSourceResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:status:GetDataSourceResponse', getDataSourceResponse_status - The current status of the DataSource. This element can have one of the following values:

PENDING - Amazon ML submitted a request to create a DataSource.
INPROGRESS - The creation process is underway.
FAILED - The request to create a DataSource did not run to completion. It is not usable.
COMPLETED - The creation process completed successfully.
DELETED - The DataSource is marked as deleted. It is not usable.

$sel:numberOfFiles:GetDataSourceResponse', getDataSourceResponse_numberOfFiles - The number of data files referenced by the DataSource.

$sel:lastUpdatedAt:GetDataSourceResponse', getDataSourceResponse_lastUpdatedAt - The time of the most recent edit to the DataSource. The time is expressed in epoch time.

$sel:createdAt:GetDataSourceResponse', getDataSourceResponse_createdAt - The time that the DataSource was created. The time is expressed in epoch time.

$sel:computeTime:GetDataSourceResponse', getDataSourceResponse_computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the DataSource, normalized and scaled on computation resources. ComputeTime is only available if the DataSource is in the COMPLETED state and the ComputeStatistics is set to true.

$sel:dataSourceId:GetDataSource', getDataSourceResponse_dataSourceId - The ID assigned to the DataSource at creation. This value should be identical to the value of the DataSourceId in the request.

$sel:rDSMetadata:GetDataSourceResponse', getDataSourceResponse_rDSMetadata - Undocumented member.

$sel:dataSizeInBytes:GetDataSourceResponse', getDataSourceResponse_dataSizeInBytes - The total size of observations in the data files.

$sel:dataSourceSchema:GetDataSourceResponse', getDataSourceResponse_dataSourceSchema - The schema used by all of the data files of this DataSource.

Note: This parameter is provided as part of the verbose format.

$sel:startedAt:GetDataSourceResponse', getDataSourceResponse_startedAt - The epoch time when Amazon Machine Learning marked the DataSource as INPROGRESS. StartedAt isn't available if the DataSource is in the PENDING state.

$sel:finishedAt:GetDataSourceResponse', getDataSourceResponse_finishedAt - The epoch time when Amazon Machine Learning marked the DataSource as COMPLETED or FAILED. FinishedAt is only available when the DataSource is in the COMPLETED or FAILED state.

$sel:createdByIamUser:GetDataSourceResponse', getDataSourceResponse_createdByIamUser - The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

$sel:name:GetDataSourceResponse', getDataSourceResponse_name - A user-supplied name or description of the DataSource.

$sel:logUri:GetDataSourceResponse', getDataSourceResponse_logUri - A link to the file containing logs of CreateDataSourceFrom* operations.

$sel:dataLocationS3:GetDataSourceResponse', getDataSourceResponse_dataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

$sel:computeStatistics:GetDataSourceResponse', getDataSourceResponse_computeStatistics - The parameter is true if statistics need to be generated from the observation data.

$sel:message:GetDataSourceResponse', getDataSourceResponse_message - The user-supplied description of the most recent details about creating the DataSource.

$sel:redshiftMetadata:GetDataSourceResponse', getDataSourceResponse_redshiftMetadata - Undocumented member.

$sel:dataRearrangement:GetDataSourceResponse', getDataSourceResponse_dataRearrangement - A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.

$sel:roleARN:GetDataSourceResponse', getDataSourceResponse_roleARN - Undocumented member.

$sel:httpStatus:GetDataSourceResponse', getDataSourceResponse_httpStatus - The response's http status code.

UpdateEvaluation

data UpdateEvaluation Source #

See: newUpdateEvaluation smart constructor.

Constructors

UpdateEvaluation' Text Text

Instances

Instances details

Eq UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods (==) :: UpdateEvaluation -> UpdateEvaluation -> Bool # (/=) :: UpdateEvaluation -> UpdateEvaluation -> Bool #
Read UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods readsPrec :: Int -> ReadS UpdateEvaluation # readList :: ReadS [UpdateEvaluation] # readPrec :: ReadPrec UpdateEvaluation # readListPrec :: ReadPrec [UpdateEvaluation] #
Show UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods showsPrec :: Int -> UpdateEvaluation -> ShowS # show :: UpdateEvaluation -> String # showList :: [UpdateEvaluation] -> ShowS #
Generic UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Associated Types type Rep UpdateEvaluation :: Type -> Type # Methods from :: UpdateEvaluation -> Rep UpdateEvaluation x # to :: Rep UpdateEvaluation x -> UpdateEvaluation #
NFData UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods rnf :: UpdateEvaluation -> () #
Hashable UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods hashWithSalt :: Int -> UpdateEvaluation -> Int # hash :: UpdateEvaluation -> Int #
ToJSON UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods toJSON :: UpdateEvaluation -> Value # toEncoding :: UpdateEvaluation -> Encoding # toJSONList :: [UpdateEvaluation] -> Value # toEncodingList :: [UpdateEvaluation] -> Encoding #
AWSRequest UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Associated Types type AWSResponse UpdateEvaluation # Methods request :: UpdateEvaluation -> Request UpdateEvaluation # response :: MonadResource m => Logger -> Service -> Proxy UpdateEvaluation -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse UpdateEvaluation))) #
ToHeaders UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods toHeaders :: UpdateEvaluation -> [Header] #
ToPath UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods toPath :: UpdateEvaluation -> ByteString #
ToQuery UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods toQuery :: UpdateEvaluation -> QueryString #
type Rep UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation type Rep UpdateEvaluation = D1 ('MetaData "UpdateEvaluation" "Amazonka.MachineLearning.UpdateEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "UpdateEvaluation'" 'PrefixI 'True) (S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "evaluationName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse UpdateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation type AWSResponse UpdateEvaluation = UpdateEvaluationResponse

newUpdateEvaluation Source #

Arguments

:: Text	`$sel:evaluationId:UpdateEvaluation'`
-> Text	`$sel:evaluationName:UpdateEvaluation'`
-> UpdateEvaluation

Create a value of UpdateEvaluation with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:evaluationId:UpdateEvaluation', updateEvaluation_evaluationId - The ID assigned to the Evaluation during creation.

$sel:evaluationName:UpdateEvaluation', updateEvaluation_evaluationName - A new user-supplied name or description of the Evaluation that will replace the current content.

data UpdateEvaluationResponse Source #

Represents the output of an UpdateEvaluation operation.

You can see the updated content by using the GetEvaluation operation.

See: newUpdateEvaluationResponse smart constructor.

Constructors

UpdateEvaluationResponse' (Maybe Text) Int

Instances

Instances details

Eq UpdateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods (==) :: UpdateEvaluationResponse -> UpdateEvaluationResponse -> Bool # (/=) :: UpdateEvaluationResponse -> UpdateEvaluationResponse -> Bool #
Read UpdateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods readsPrec :: Int -> ReadS UpdateEvaluationResponse # readList :: ReadS [UpdateEvaluationResponse] # readPrec :: ReadPrec UpdateEvaluationResponse # readListPrec :: ReadPrec [UpdateEvaluationResponse] #
Show UpdateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods showsPrec :: Int -> UpdateEvaluationResponse -> ShowS # show :: UpdateEvaluationResponse -> String # showList :: [UpdateEvaluationResponse] -> ShowS #
Generic UpdateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Associated Types type Rep UpdateEvaluationResponse :: Type -> Type # Methods from :: UpdateEvaluationResponse -> Rep UpdateEvaluationResponse x # to :: Rep UpdateEvaluationResponse x -> UpdateEvaluationResponse #
NFData UpdateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation Methods rnf :: UpdateEvaluationResponse -> () #
type Rep UpdateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateEvaluation type Rep UpdateEvaluationResponse = D1 ('MetaData "UpdateEvaluationResponse" "Amazonka.MachineLearning.UpdateEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "UpdateEvaluationResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newUpdateEvaluationResponse Source #

Arguments

:: Int	`$sel:httpStatus:UpdateEvaluationResponse'`
-> UpdateEvaluationResponse

Create a value of UpdateEvaluationResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:evaluationId:UpdateEvaluation', updateEvaluationResponse_evaluationId - The ID assigned to the Evaluation during creation. This value should be identical to the value of the Evaluation in the request.

$sel:httpStatus:UpdateEvaluationResponse', updateEvaluationResponse_httpStatus - The response's http status code.

DeleteEvaluation

data DeleteEvaluation Source #

See: newDeleteEvaluation smart constructor.

Constructors

DeleteEvaluation' Text

Instances

Instances details

Eq DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods (==) :: DeleteEvaluation -> DeleteEvaluation -> Bool # (/=) :: DeleteEvaluation -> DeleteEvaluation -> Bool #
Read DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods readsPrec :: Int -> ReadS DeleteEvaluation # readList :: ReadS [DeleteEvaluation] # readPrec :: ReadPrec DeleteEvaluation # readListPrec :: ReadPrec [DeleteEvaluation] #
Show DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods showsPrec :: Int -> DeleteEvaluation -> ShowS # show :: DeleteEvaluation -> String # showList :: [DeleteEvaluation] -> ShowS #
Generic DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Associated Types type Rep DeleteEvaluation :: Type -> Type # Methods from :: DeleteEvaluation -> Rep DeleteEvaluation x # to :: Rep DeleteEvaluation x -> DeleteEvaluation #
NFData DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods rnf :: DeleteEvaluation -> () #
Hashable DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods hashWithSalt :: Int -> DeleteEvaluation -> Int # hash :: DeleteEvaluation -> Int #
ToJSON DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods toJSON :: DeleteEvaluation -> Value # toEncoding :: DeleteEvaluation -> Encoding # toJSONList :: [DeleteEvaluation] -> Value # toEncodingList :: [DeleteEvaluation] -> Encoding #
AWSRequest DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Associated Types type AWSResponse DeleteEvaluation # Methods request :: DeleteEvaluation -> Request DeleteEvaluation # response :: MonadResource m => Logger -> Service -> Proxy DeleteEvaluation -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DeleteEvaluation))) #
ToHeaders DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods toHeaders :: DeleteEvaluation -> [Header] #
ToPath DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods toPath :: DeleteEvaluation -> ByteString #
ToQuery DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods toQuery :: DeleteEvaluation -> QueryString #
type Rep DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation type Rep DeleteEvaluation = D1 ('MetaData "DeleteEvaluation" "Amazonka.MachineLearning.DeleteEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteEvaluation'" 'PrefixI 'True) (S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse DeleteEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation type AWSResponse DeleteEvaluation = DeleteEvaluationResponse

newDeleteEvaluation Source #

Arguments

:: Text	`$sel:evaluationId:DeleteEvaluation'`
-> DeleteEvaluation

Create a value of DeleteEvaluation with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:evaluationId:DeleteEvaluation', deleteEvaluation_evaluationId - A user-supplied ID that uniquely identifies the Evaluation to delete.

data DeleteEvaluationResponse Source #

Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.

You can use the GetEvaluation operation and check the value of the Status parameter to see whether an Evaluation is marked as DELETED.

See: newDeleteEvaluationResponse smart constructor.

Constructors

DeleteEvaluationResponse' (Maybe Text) Int

Instances

Instances details

Eq DeleteEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods (==) :: DeleteEvaluationResponse -> DeleteEvaluationResponse -> Bool # (/=) :: DeleteEvaluationResponse -> DeleteEvaluationResponse -> Bool #
Read DeleteEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods readsPrec :: Int -> ReadS DeleteEvaluationResponse # readList :: ReadS [DeleteEvaluationResponse] # readPrec :: ReadPrec DeleteEvaluationResponse # readListPrec :: ReadPrec [DeleteEvaluationResponse] #
Show DeleteEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods showsPrec :: Int -> DeleteEvaluationResponse -> ShowS # show :: DeleteEvaluationResponse -> String # showList :: [DeleteEvaluationResponse] -> ShowS #
Generic DeleteEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Associated Types type Rep DeleteEvaluationResponse :: Type -> Type # Methods from :: DeleteEvaluationResponse -> Rep DeleteEvaluationResponse x # to :: Rep DeleteEvaluationResponse x -> DeleteEvaluationResponse #
NFData DeleteEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation Methods rnf :: DeleteEvaluationResponse -> () #
type Rep DeleteEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteEvaluation type Rep DeleteEvaluationResponse = D1 ('MetaData "DeleteEvaluationResponse" "Amazonka.MachineLearning.DeleteEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteEvaluationResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newDeleteEvaluationResponse Source #

Arguments

:: Int	`$sel:httpStatus:DeleteEvaluationResponse'`
-> DeleteEvaluationResponse

Create a value of DeleteEvaluationResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:evaluationId:DeleteEvaluation', deleteEvaluationResponse_evaluationId - A user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

$sel:httpStatus:DeleteEvaluationResponse', deleteEvaluationResponse_httpStatus - The response's http status code.

DeleteMLModel

data DeleteMLModel Source #

See: newDeleteMLModel smart constructor.

Constructors

DeleteMLModel' Text

Instances

Instances details

Eq DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods (==) :: DeleteMLModel -> DeleteMLModel -> Bool # (/=) :: DeleteMLModel -> DeleteMLModel -> Bool #
Read DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods readsPrec :: Int -> ReadS DeleteMLModel # readList :: ReadS [DeleteMLModel] # readPrec :: ReadPrec DeleteMLModel # readListPrec :: ReadPrec [DeleteMLModel] #
Show DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods showsPrec :: Int -> DeleteMLModel -> ShowS # show :: DeleteMLModel -> String # showList :: [DeleteMLModel] -> ShowS #
Generic DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Associated Types type Rep DeleteMLModel :: Type -> Type # Methods from :: DeleteMLModel -> Rep DeleteMLModel x # to :: Rep DeleteMLModel x -> DeleteMLModel #
NFData DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods rnf :: DeleteMLModel -> () #
Hashable DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods hashWithSalt :: Int -> DeleteMLModel -> Int # hash :: DeleteMLModel -> Int #
ToJSON DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods toJSON :: DeleteMLModel -> Value # toEncoding :: DeleteMLModel -> Encoding # toJSONList :: [DeleteMLModel] -> Value # toEncodingList :: [DeleteMLModel] -> Encoding #
AWSRequest DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Associated Types type AWSResponse DeleteMLModel # Methods request :: DeleteMLModel -> Request DeleteMLModel # response :: MonadResource m => Logger -> Service -> Proxy DeleteMLModel -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DeleteMLModel))) #
ToHeaders DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods toHeaders :: DeleteMLModel -> [Header] #
ToPath DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods toPath :: DeleteMLModel -> ByteString #
ToQuery DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods toQuery :: DeleteMLModel -> QueryString #
type Rep DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel type Rep DeleteMLModel = D1 ('MetaData "DeleteMLModel" "Amazonka.MachineLearning.DeleteMLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteMLModel'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse DeleteMLModel Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel type AWSResponse DeleteMLModel = DeleteMLModelResponse

newDeleteMLModel Source #

Arguments

:: Text	`$sel:mLModelId:DeleteMLModel'`
-> DeleteMLModel

Create a value of DeleteMLModel with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelId:DeleteMLModel', deleteMLModel_mLModelId - A user-supplied ID that uniquely identifies the MLModel.

data DeleteMLModelResponse Source #

Represents the output of a DeleteMLModel operation.

You can use the GetMLModel operation and check the value of the Status parameter to see whether an MLModel is marked as DELETED.

See: newDeleteMLModelResponse smart constructor.

Constructors

DeleteMLModelResponse' (Maybe Text) Int

Instances

Instances details

Eq DeleteMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods (==) :: DeleteMLModelResponse -> DeleteMLModelResponse -> Bool # (/=) :: DeleteMLModelResponse -> DeleteMLModelResponse -> Bool #
Read DeleteMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods readsPrec :: Int -> ReadS DeleteMLModelResponse # readList :: ReadS [DeleteMLModelResponse] # readPrec :: ReadPrec DeleteMLModelResponse # readListPrec :: ReadPrec [DeleteMLModelResponse] #
Show DeleteMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods showsPrec :: Int -> DeleteMLModelResponse -> ShowS # show :: DeleteMLModelResponse -> String # showList :: [DeleteMLModelResponse] -> ShowS #
Generic DeleteMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Associated Types type Rep DeleteMLModelResponse :: Type -> Type # Methods from :: DeleteMLModelResponse -> Rep DeleteMLModelResponse x # to :: Rep DeleteMLModelResponse x -> DeleteMLModelResponse #
NFData DeleteMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel Methods rnf :: DeleteMLModelResponse -> () #
type Rep DeleteMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteMLModel type Rep DeleteMLModelResponse = D1 ('MetaData "DeleteMLModelResponse" "Amazonka.MachineLearning.DeleteMLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteMLModelResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newDeleteMLModelResponse Source #

Arguments

:: Int	`$sel:httpStatus:DeleteMLModelResponse'`
-> DeleteMLModelResponse

Create a value of DeleteMLModelResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelId:DeleteMLModel', deleteMLModelResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelID in the request.

$sel:httpStatus:DeleteMLModelResponse', deleteMLModelResponse_httpStatus - The response's http status code.

UpdateMLModel

data UpdateMLModel Source #

See: newUpdateMLModel smart constructor.

Constructors

UpdateMLModel' (Maybe Text) (Maybe Double) Text

Instances

Instances details

Eq UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods (==) :: UpdateMLModel -> UpdateMLModel -> Bool # (/=) :: UpdateMLModel -> UpdateMLModel -> Bool #
Read UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods readsPrec :: Int -> ReadS UpdateMLModel # readList :: ReadS [UpdateMLModel] # readPrec :: ReadPrec UpdateMLModel # readListPrec :: ReadPrec [UpdateMLModel] #
Show UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods showsPrec :: Int -> UpdateMLModel -> ShowS # show :: UpdateMLModel -> String # showList :: [UpdateMLModel] -> ShowS #
Generic UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Associated Types type Rep UpdateMLModel :: Type -> Type # Methods from :: UpdateMLModel -> Rep UpdateMLModel x # to :: Rep UpdateMLModel x -> UpdateMLModel #
NFData UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods rnf :: UpdateMLModel -> () #
Hashable UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods hashWithSalt :: Int -> UpdateMLModel -> Int # hash :: UpdateMLModel -> Int #
ToJSON UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods toJSON :: UpdateMLModel -> Value # toEncoding :: UpdateMLModel -> Encoding # toJSONList :: [UpdateMLModel] -> Value # toEncodingList :: [UpdateMLModel] -> Encoding #
AWSRequest UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Associated Types type AWSResponse UpdateMLModel # Methods request :: UpdateMLModel -> Request UpdateMLModel # response :: MonadResource m => Logger -> Service -> Proxy UpdateMLModel -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse UpdateMLModel))) #
ToHeaders UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods toHeaders :: UpdateMLModel -> [Header] #
ToPath UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods toPath :: UpdateMLModel -> ByteString #
ToQuery UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods toQuery :: UpdateMLModel -> QueryString #
type Rep UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel type Rep UpdateMLModel = D1 ('MetaData "UpdateMLModel" "Amazonka.MachineLearning.UpdateMLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "UpdateMLModel'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "scoreThreshold") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :: S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text))))
type AWSResponse UpdateMLModel Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel type AWSResponse UpdateMLModel = UpdateMLModelResponse

newUpdateMLModel Source #

Arguments

:: Text	`$sel:mLModelId:UpdateMLModel'`
-> UpdateMLModel

Create a value of UpdateMLModel with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelName:UpdateMLModel', updateMLModel_mLModelName - A user-supplied name or description of the MLModel.

$sel:scoreThreshold:UpdateMLModel', updateMLModel_scoreThreshold - The ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the ScoreThreshold receive a positive result from the MLModel, such as true. Output values less than the ScoreThreshold receive a negative response from the MLModel, such as false.

$sel:mLModelId:UpdateMLModel', updateMLModel_mLModelId - The ID assigned to the MLModel during creation.

data UpdateMLModelResponse Source #

Represents the output of an UpdateMLModel operation.

You can see the updated content by using the GetMLModel operation.

See: newUpdateMLModelResponse smart constructor.

Constructors

UpdateMLModelResponse' (Maybe Text) Int

Instances

Instances details

Eq UpdateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods (==) :: UpdateMLModelResponse -> UpdateMLModelResponse -> Bool # (/=) :: UpdateMLModelResponse -> UpdateMLModelResponse -> Bool #
Read UpdateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods readsPrec :: Int -> ReadS UpdateMLModelResponse # readList :: ReadS [UpdateMLModelResponse] # readPrec :: ReadPrec UpdateMLModelResponse # readListPrec :: ReadPrec [UpdateMLModelResponse] #
Show UpdateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods showsPrec :: Int -> UpdateMLModelResponse -> ShowS # show :: UpdateMLModelResponse -> String # showList :: [UpdateMLModelResponse] -> ShowS #
Generic UpdateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Associated Types type Rep UpdateMLModelResponse :: Type -> Type # Methods from :: UpdateMLModelResponse -> Rep UpdateMLModelResponse x # to :: Rep UpdateMLModelResponse x -> UpdateMLModelResponse #
NFData UpdateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel Methods rnf :: UpdateMLModelResponse -> () #
type Rep UpdateMLModelResponse Source #
Instance details Defined in Amazonka.MachineLearning.UpdateMLModel type Rep UpdateMLModelResponse = D1 ('MetaData "UpdateMLModelResponse" "Amazonka.MachineLearning.UpdateMLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "UpdateMLModelResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newUpdateMLModelResponse Source #

Arguments

:: Int	`$sel:httpStatus:UpdateMLModelResponse'`
-> UpdateMLModelResponse

Create a value of UpdateMLModelResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelId:UpdateMLModel', updateMLModelResponse_mLModelId - The ID assigned to the MLModel during creation. This value should be identical to the value of the MLModelID in the request.

$sel:httpStatus:UpdateMLModelResponse', updateMLModelResponse_httpStatus - The response's http status code.

GetBatchPrediction

data GetBatchPrediction Source #

See: newGetBatchPrediction smart constructor.

Constructors

GetBatchPrediction' Text

Instances

Instances details

Eq GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods (==) :: GetBatchPrediction -> GetBatchPrediction -> Bool # (/=) :: GetBatchPrediction -> GetBatchPrediction -> Bool #
Read GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods readsPrec :: Int -> ReadS GetBatchPrediction # readList :: ReadS [GetBatchPrediction] # readPrec :: ReadPrec GetBatchPrediction # readListPrec :: ReadPrec [GetBatchPrediction] #
Show GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods showsPrec :: Int -> GetBatchPrediction -> ShowS # show :: GetBatchPrediction -> String # showList :: [GetBatchPrediction] -> ShowS #
Generic GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Associated Types type Rep GetBatchPrediction :: Type -> Type # Methods from :: GetBatchPrediction -> Rep GetBatchPrediction x # to :: Rep GetBatchPrediction x -> GetBatchPrediction #
NFData GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods rnf :: GetBatchPrediction -> () #
Hashable GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods hashWithSalt :: Int -> GetBatchPrediction -> Int # hash :: GetBatchPrediction -> Int #
ToJSON GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods toJSON :: GetBatchPrediction -> Value # toEncoding :: GetBatchPrediction -> Encoding # toJSONList :: [GetBatchPrediction] -> Value # toEncodingList :: [GetBatchPrediction] -> Encoding #
AWSRequest GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Associated Types type AWSResponse GetBatchPrediction # Methods request :: GetBatchPrediction -> Request GetBatchPrediction # response :: MonadResource m => Logger -> Service -> Proxy GetBatchPrediction -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse GetBatchPrediction))) #
ToHeaders GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods toHeaders :: GetBatchPrediction -> [Header] #
ToPath GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods toPath :: GetBatchPrediction -> ByteString #
ToQuery GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods toQuery :: GetBatchPrediction -> QueryString #
type Rep GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction type Rep GetBatchPrediction = D1 ('MetaData "GetBatchPrediction" "Amazonka.MachineLearning.GetBatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "GetBatchPrediction'" 'PrefixI 'True) (S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse GetBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction type AWSResponse GetBatchPrediction = GetBatchPredictionResponse

newGetBatchPrediction Source #

Arguments

:: Text	`$sel:batchPredictionId:GetBatchPrediction'`
-> GetBatchPrediction

Create a value of GetBatchPrediction with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:batchPredictionId:GetBatchPrediction', getBatchPrediction_batchPredictionId - An ID assigned to the BatchPrediction at creation.

data GetBatchPredictionResponse Source #

Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.

See: newGetBatchPredictionResponse smart constructor.

Constructors

GetBatchPredictionResponse' (Maybe EntityStatus) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) Int

Instances

Instances details

Eq GetBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods (==) :: GetBatchPredictionResponse -> GetBatchPredictionResponse -> Bool # (/=) :: GetBatchPredictionResponse -> GetBatchPredictionResponse -> Bool #
Read GetBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods readsPrec :: Int -> ReadS GetBatchPredictionResponse # readList :: ReadS [GetBatchPredictionResponse] # readPrec :: ReadPrec GetBatchPredictionResponse # readListPrec :: ReadPrec [GetBatchPredictionResponse] #
Show GetBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods showsPrec :: Int -> GetBatchPredictionResponse -> ShowS # show :: GetBatchPredictionResponse -> String # showList :: [GetBatchPredictionResponse] -> ShowS #
Generic GetBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Associated Types type Rep GetBatchPredictionResponse :: Type -> Type # Methods from :: GetBatchPredictionResponse -> Rep GetBatchPredictionResponse x # to :: Rep GetBatchPredictionResponse x -> GetBatchPredictionResponse #
NFData GetBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction Methods rnf :: GetBatchPredictionResponse -> () #
type Rep GetBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetBatchPrediction type Rep GetBatchPredictionResponse = D1 ('MetaData "GetBatchPredictionResponse" "Amazonka.MachineLearning.GetBatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "GetBatchPredictionResponse'" 'PrefixI 'True) ((((S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EntityStatus)) :: S1 ('MetaSel ('Just "lastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))) :: (S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "computeTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)))) :: ((S1 ('MetaSel ('Just "inputDataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "batchPredictionDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "totalRecordCount") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: S1 ('MetaSel ('Just "startedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)))))) :: (((S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "finishedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))) :: (S1 ('MetaSel ('Just "invalidRecordCount") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: S1 ('MetaSel ('Just "createdByIamUser") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :: ((S1 ('MetaSel ('Just "name") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "logUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "outputUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))))))

newGetBatchPredictionResponse Source #

Arguments

:: Int	`$sel:httpStatus:GetBatchPredictionResponse'`
-> GetBatchPredictionResponse

Create a value of GetBatchPredictionResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:status:GetBatchPredictionResponse', getBatchPredictionResponse_status - The status of the BatchPrediction, which can be one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.
INPROGRESS - The batch predictions are in progress.
FAILED - The request to perform a batch prediction did not run to completion. It is not usable.
COMPLETED - The batch prediction process completed successfully.
DELETED - The BatchPrediction is marked as deleted. It is not usable.

$sel:lastUpdatedAt:GetBatchPredictionResponse', getBatchPredictionResponse_lastUpdatedAt - The time of the most recent edit to BatchPrediction. The time is expressed in epoch time.

$sel:createdAt:GetBatchPredictionResponse', getBatchPredictionResponse_createdAt - The time when the BatchPrediction was created. The time is expressed in epoch time.

$sel:computeTime:GetBatchPredictionResponse', getBatchPredictionResponse_computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the BatchPrediction, normalized and scaled on computation resources. ComputeTime is only available if the BatchPrediction is in the COMPLETED state.

$sel:inputDataLocationS3:GetBatchPredictionResponse', getBatchPredictionResponse_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

$sel:mLModelId:GetBatchPredictionResponse', getBatchPredictionResponse_mLModelId - The ID of the MLModel that generated predictions for the BatchPrediction request.

$sel:batchPredictionDataSourceId:GetBatchPredictionResponse', getBatchPredictionResponse_batchPredictionDataSourceId - The ID of the DataSource that was used to create the BatchPrediction.

$sel:totalRecordCount:GetBatchPredictionResponse', getBatchPredictionResponse_totalRecordCount - The number of total records that Amazon Machine Learning saw while processing the BatchPrediction.

$sel:startedAt:GetBatchPredictionResponse', getBatchPredictionResponse_startedAt - The epoch time when Amazon Machine Learning marked the BatchPrediction as INPROGRESS. StartedAt isn't available if the BatchPrediction is in the PENDING state.

$sel:batchPredictionId:GetBatchPrediction', getBatchPredictionResponse_batchPredictionId - An ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

$sel:finishedAt:GetBatchPredictionResponse', getBatchPredictionResponse_finishedAt - The epoch time when Amazon Machine Learning marked the BatchPrediction as COMPLETED or FAILED. FinishedAt is only available when the BatchPrediction is in the COMPLETED or FAILED state.

$sel:invalidRecordCount:GetBatchPredictionResponse', getBatchPredictionResponse_invalidRecordCount - The number of invalid records that Amazon Machine Learning saw while processing the BatchPrediction.

$sel:createdByIamUser:GetBatchPredictionResponse', getBatchPredictionResponse_createdByIamUser - The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

$sel:name:GetBatchPredictionResponse', getBatchPredictionResponse_name - A user-supplied name or description of the BatchPrediction.

$sel:logUri:GetBatchPredictionResponse', getBatchPredictionResponse_logUri - A link to the file that contains logs of the CreateBatchPrediction operation.

$sel:message:GetBatchPredictionResponse', getBatchPredictionResponse_message - A description of the most recent details about processing the batch prediction request.

$sel:outputUri:GetBatchPredictionResponse', getBatchPredictionResponse_outputUri - The location of an Amazon S3 bucket or directory to receive the operation results.

$sel:httpStatus:GetBatchPredictionResponse', getBatchPredictionResponse_httpStatus - The response's http status code.

DescribeBatchPredictions (Paginated)

data DescribeBatchPredictions Source #

See: newDescribeBatchPredictions smart constructor.

Constructors

DescribeBatchPredictions' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) (Maybe Natural) (Maybe Text) (Maybe BatchPredictionFilterVariable) (Maybe Text)

Instances

Instances details

Eq DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods (==) :: DescribeBatchPredictions -> DescribeBatchPredictions -> Bool # (/=) :: DescribeBatchPredictions -> DescribeBatchPredictions -> Bool #
Read DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods readsPrec :: Int -> ReadS DescribeBatchPredictions # readList :: ReadS [DescribeBatchPredictions] # readPrec :: ReadPrec DescribeBatchPredictions # readListPrec :: ReadPrec [DescribeBatchPredictions] #
Show DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods showsPrec :: Int -> DescribeBatchPredictions -> ShowS # show :: DescribeBatchPredictions -> String # showList :: [DescribeBatchPredictions] -> ShowS #
Generic DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Associated Types type Rep DescribeBatchPredictions :: Type -> Type # Methods from :: DescribeBatchPredictions -> Rep DescribeBatchPredictions x # to :: Rep DescribeBatchPredictions x -> DescribeBatchPredictions #
NFData DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods rnf :: DescribeBatchPredictions -> () #
Hashable DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods hashWithSalt :: Int -> DescribeBatchPredictions -> Int # hash :: DescribeBatchPredictions -> Int #
ToJSON DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods toJSON :: DescribeBatchPredictions -> Value # toEncoding :: DescribeBatchPredictions -> Encoding # toJSONList :: [DescribeBatchPredictions] -> Value # toEncodingList :: [DescribeBatchPredictions] -> Encoding #
AWSPager DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods page :: DescribeBatchPredictions -> AWSResponse DescribeBatchPredictions -> Maybe DescribeBatchPredictions #
AWSRequest DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Associated Types type AWSResponse DescribeBatchPredictions # Methods request :: DescribeBatchPredictions -> Request DescribeBatchPredictions # response :: MonadResource m => Logger -> Service -> Proxy DescribeBatchPredictions -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DescribeBatchPredictions))) #
ToHeaders DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods toHeaders :: DescribeBatchPredictions -> [Header] #
ToPath DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods toPath :: DescribeBatchPredictions -> ByteString #
ToQuery DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods toQuery :: DescribeBatchPredictions -> QueryString #
type Rep DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions type Rep DescribeBatchPredictions = D1 ('MetaData "DescribeBatchPredictions" "Amazonka.MachineLearning.DescribeBatchPredictions" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeBatchPredictions'" 'PrefixI 'True) (((S1 ('MetaSel ('Just "eq") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "ge") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "prefix") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "gt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "ne") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))) :: ((S1 ('MetaSel ('Just "nextToken") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "sortOrder") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe SortOrder)) :: S1 ('MetaSel ('Just "limit") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Natural)))) :: (S1 ('MetaSel ('Just "lt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "filterVariable") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe BatchPredictionFilterVariable)) :: S1 ('MetaSel ('Just "le") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))))
type AWSResponse DescribeBatchPredictions Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions type AWSResponse DescribeBatchPredictions = DescribeBatchPredictionsResponse

newDescribeBatchPredictions :: DescribeBatchPredictions Source #

Create a value of DescribeBatchPredictions with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:eq:DescribeBatchPredictions', describeBatchPredictions_eq - The equal to operator. The BatchPrediction results will have FilterVariable values that exactly match the value specified with EQ.

$sel:ge:DescribeBatchPredictions', describeBatchPredictions_ge - The greater than or equal to operator. The BatchPrediction results will have FilterVariable values that are greater than or equal to the value specified with GE.

$sel:prefix:DescribeBatchPredictions', describeBatchPredictions_prefix - A string that is found at the beginning of a variable, such as Name or Id.

For example, a Batch Prediction operation could have the Name 2014-09-09-HolidayGiftMailer. To search for this BatchPrediction, select Name for the FilterVariable and any of the following strings for the Prefix:

2014-09
2014-09-09
2014-09-09-Holiday

$sel:gt:DescribeBatchPredictions', describeBatchPredictions_gt - The greater than operator. The BatchPrediction results will have FilterVariable values that are greater than the value specified with GT.

$sel:ne:DescribeBatchPredictions', describeBatchPredictions_ne - The not equal to operator. The BatchPrediction results will have FilterVariable values not equal to the value specified with NE.

$sel:nextToken:DescribeBatchPredictions', describeBatchPredictions_nextToken - An ID of the page in the paginated results.

$sel:sortOrder:DescribeBatchPredictions', describeBatchPredictions_sortOrder - A two-value parameter that determines the sequence of the resulting list of MLModels.

asc - Arranges the list in ascending order (A-Z, 0-9).
dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

$sel:limit:DescribeBatchPredictions', describeBatchPredictions_limit - The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.

$sel:lt:DescribeBatchPredictions', describeBatchPredictions_lt - The less than operator. The BatchPrediction results will have FilterVariable values that are less than the value specified with LT.

$sel:filterVariable:DescribeBatchPredictions', describeBatchPredictions_filterVariable - Use one of the following variables to filter a list of BatchPrediction:

CreatedAt - Sets the search criteria to the BatchPrediction creation date.
Status - Sets the search criteria to the BatchPrediction status.
Name - Sets the search criteria to the contents of the BatchPrediction ____ Name.
IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation.
MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.
DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction.
DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.

$sel:le:DescribeBatchPredictions', describeBatchPredictions_le - The less than or equal to operator. The BatchPrediction results will have FilterVariable values that are less than or equal to the value specified with LE.

data DescribeBatchPredictionsResponse Source #

Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.

See: newDescribeBatchPredictionsResponse smart constructor.

Constructors

DescribeBatchPredictionsResponse' (Maybe [BatchPrediction]) (Maybe Text) Int

Instances

Instances details

Eq DescribeBatchPredictionsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods (==) :: DescribeBatchPredictionsResponse -> DescribeBatchPredictionsResponse -> Bool # (/=) :: DescribeBatchPredictionsResponse -> DescribeBatchPredictionsResponse -> Bool #
Read DescribeBatchPredictionsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods readsPrec :: Int -> ReadS DescribeBatchPredictionsResponse # readList :: ReadS [DescribeBatchPredictionsResponse] # readPrec :: ReadPrec DescribeBatchPredictionsResponse # readListPrec :: ReadPrec [DescribeBatchPredictionsResponse] #
Show DescribeBatchPredictionsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods showsPrec :: Int -> DescribeBatchPredictionsResponse -> ShowS # show :: DescribeBatchPredictionsResponse -> String # showList :: [DescribeBatchPredictionsResponse] -> ShowS #
Generic DescribeBatchPredictionsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Associated Types type Rep DescribeBatchPredictionsResponse :: Type -> Type # Methods from :: DescribeBatchPredictionsResponse -> Rep DescribeBatchPredictionsResponse x # to :: Rep DescribeBatchPredictionsResponse x -> DescribeBatchPredictionsResponse #
NFData DescribeBatchPredictionsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions Methods rnf :: DescribeBatchPredictionsResponse -> () #
type Rep DescribeBatchPredictionsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeBatchPredictions type Rep DescribeBatchPredictionsResponse = D1 ('MetaData "DescribeBatchPredictionsResponse" "Amazonka.MachineLearning.DescribeBatchPredictions" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeBatchPredictionsResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "results") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [BatchPrediction])) :: (S1 ('MetaSel ('Just "nextToken") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newDescribeBatchPredictionsResponse Source #

Arguments

:: Int	`$sel:httpStatus:DescribeBatchPredictionsResponse'`
-> DescribeBatchPredictionsResponse

Create a value of DescribeBatchPredictionsResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:results:DescribeBatchPredictionsResponse', describeBatchPredictionsResponse_results - A list of BatchPrediction objects that meet the search criteria.

$sel:nextToken:DescribeBatchPredictions', describeBatchPredictionsResponse_nextToken - The ID of the next page in the paginated results that indicates at least one more page follows.

$sel:httpStatus:DescribeBatchPredictionsResponse', describeBatchPredictionsResponse_httpStatus - The response's http status code.

CreateDataSourceFromRDS

data CreateDataSourceFromRDS Source #

See: newCreateDataSourceFromRDS smart constructor.

Constructors

CreateDataSourceFromRDS' (Maybe Text) (Maybe Bool) Text RDSDataSpec Text

Instances

Instances details

Eq CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods (==) :: CreateDataSourceFromRDS -> CreateDataSourceFromRDS -> Bool # (/=) :: CreateDataSourceFromRDS -> CreateDataSourceFromRDS -> Bool #
Read CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods readsPrec :: Int -> ReadS CreateDataSourceFromRDS # readList :: ReadS [CreateDataSourceFromRDS] # readPrec :: ReadPrec CreateDataSourceFromRDS # readListPrec :: ReadPrec [CreateDataSourceFromRDS] #
Show CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods showsPrec :: Int -> CreateDataSourceFromRDS -> ShowS # show :: CreateDataSourceFromRDS -> String # showList :: [CreateDataSourceFromRDS] -> ShowS #
Generic CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Associated Types type Rep CreateDataSourceFromRDS :: Type -> Type # Methods from :: CreateDataSourceFromRDS -> Rep CreateDataSourceFromRDS x # to :: Rep CreateDataSourceFromRDS x -> CreateDataSourceFromRDS #
NFData CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods rnf :: CreateDataSourceFromRDS -> () #
Hashable CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods hashWithSalt :: Int -> CreateDataSourceFromRDS -> Int # hash :: CreateDataSourceFromRDS -> Int #
ToJSON CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods toJSON :: CreateDataSourceFromRDS -> Value # toEncoding :: CreateDataSourceFromRDS -> Encoding # toJSONList :: [CreateDataSourceFromRDS] -> Value # toEncodingList :: [CreateDataSourceFromRDS] -> Encoding #
AWSRequest CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Associated Types type AWSResponse CreateDataSourceFromRDS # Methods request :: CreateDataSourceFromRDS -> Request CreateDataSourceFromRDS # response :: MonadResource m => Logger -> Service -> Proxy CreateDataSourceFromRDS -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateDataSourceFromRDS))) #
ToHeaders CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods toHeaders :: CreateDataSourceFromRDS -> [Header] #
ToPath CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods toPath :: CreateDataSourceFromRDS -> ByteString #
ToQuery CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods toQuery :: CreateDataSourceFromRDS -> QueryString #
type Rep CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS type Rep CreateDataSourceFromRDS = D1 ('MetaData "CreateDataSourceFromRDS" "Amazonka.MachineLearning.CreateDataSourceFromRDS" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateDataSourceFromRDS'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "dataSourceName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "computeStatistics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool))) :: (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: (S1 ('MetaSel ('Just "rDSData") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 RDSDataSpec) :: S1 ('MetaSel ('Just "roleARN") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))))
type AWSResponse CreateDataSourceFromRDS Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS type AWSResponse CreateDataSourceFromRDS = CreateDataSourceFromRDSResponse

newCreateDataSourceFromRDS Source #

Arguments

:: Text	`$sel:dataSourceId:CreateDataSourceFromRDS'`
-> RDSDataSpec	`$sel:rDSData:CreateDataSourceFromRDS'`
-> Text	`$sel:roleARN:CreateDataSourceFromRDS'`
-> CreateDataSourceFromRDS

Create a value of CreateDataSourceFromRDS with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceName:CreateDataSourceFromRDS', createDataSourceFromRDS_dataSourceName - A user-supplied name or description of the DataSource.

$sel:computeStatistics:CreateDataSourceFromRDS', createDataSourceFromRDS_computeStatistics - The compute statistics for a DataSource. The statistics are generated from the observation data referenced by a DataSource. Amazon ML uses the statistics internally during MLModel training. This parameter must be set to true if the @DataSource needs to be used for MLModel@ training.

$sel:dataSourceId:CreateDataSourceFromRDS', createDataSourceFromRDS_dataSourceId - A user-supplied ID that uniquely identifies the DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource.

$sel:rDSData:CreateDataSourceFromRDS', createDataSourceFromRDS_rDSData - The data specification of an Amazon RDS DataSource:

DatabaseInformation -
- DatabaseName - The name of the Amazon RDS database.
- InstanceIdentifier - A unique identifier for the Amazon RDS database instance.
DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.
ResourceRole - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see Role templates for data pipelines.
ServiceRole - A role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
SecurityInfo - The security information to use to access an RDS DB instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId, SecurityGroupIds] pair for a VPC-based RDS DB instance.
SelectSqlQuery - A query that is used to retrieve the observation data for the Datasource.
S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
DataSchemaUri - The Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.
DataRearrangement - A JSON string that represents the splitting and rearrangement requirements for the Datasource.
Sample - "{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}"

$sel:roleARN:CreateDataSourceFromRDS', createDataSourceFromRDS_roleARN - The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user's account and copy data using the SelectSqlQuery query from Amazon RDS to Amazon S3.

data CreateDataSourceFromRDSResponse Source #

Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.

The CreateDataSourceFromRDS> operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status parameter. You can inspect the Message when Status shows up as FAILED. You can also check the progress of the copy operation by going to the DataPipeline console and looking up the pipeline using the pipelineId from the describe call.

See: newCreateDataSourceFromRDSResponse smart constructor.

Constructors

CreateDataSourceFromRDSResponse' (Maybe Text) Int

Instances

Instances details

Eq CreateDataSourceFromRDSResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods (==) :: CreateDataSourceFromRDSResponse -> CreateDataSourceFromRDSResponse -> Bool # (/=) :: CreateDataSourceFromRDSResponse -> CreateDataSourceFromRDSResponse -> Bool #
Read CreateDataSourceFromRDSResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods readsPrec :: Int -> ReadS CreateDataSourceFromRDSResponse # readList :: ReadS [CreateDataSourceFromRDSResponse] # readPrec :: ReadPrec CreateDataSourceFromRDSResponse # readListPrec :: ReadPrec [CreateDataSourceFromRDSResponse] #
Show CreateDataSourceFromRDSResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods showsPrec :: Int -> CreateDataSourceFromRDSResponse -> ShowS # show :: CreateDataSourceFromRDSResponse -> String # showList :: [CreateDataSourceFromRDSResponse] -> ShowS #
Generic CreateDataSourceFromRDSResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Associated Types type Rep CreateDataSourceFromRDSResponse :: Type -> Type # Methods from :: CreateDataSourceFromRDSResponse -> Rep CreateDataSourceFromRDSResponse x # to :: Rep CreateDataSourceFromRDSResponse x -> CreateDataSourceFromRDSResponse #
NFData CreateDataSourceFromRDSResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS Methods rnf :: CreateDataSourceFromRDSResponse -> () #
type Rep CreateDataSourceFromRDSResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateDataSourceFromRDS type Rep CreateDataSourceFromRDSResponse = D1 ('MetaData "CreateDataSourceFromRDSResponse" "Amazonka.MachineLearning.CreateDataSourceFromRDS" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateDataSourceFromRDSResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newCreateDataSourceFromRDSResponse Source #

Arguments

:: Int	`$sel:httpStatus:CreateDataSourceFromRDSResponse'`
-> CreateDataSourceFromRDSResponse

Create a value of CreateDataSourceFromRDSResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSourceId:CreateDataSourceFromRDS', createDataSourceFromRDSResponse_dataSourceId - A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request.

$sel:httpStatus:CreateDataSourceFromRDSResponse', createDataSourceFromRDSResponse_httpStatus - The response's http status code.

CreateEvaluation

data CreateEvaluation Source #

See: newCreateEvaluation smart constructor.

Constructors

CreateEvaluation' (Maybe Text) Text Text Text

Instances

Instances details

Eq CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods (==) :: CreateEvaluation -> CreateEvaluation -> Bool # (/=) :: CreateEvaluation -> CreateEvaluation -> Bool #
Read CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods readsPrec :: Int -> ReadS CreateEvaluation # readList :: ReadS [CreateEvaluation] # readPrec :: ReadPrec CreateEvaluation # readListPrec :: ReadPrec [CreateEvaluation] #
Show CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods showsPrec :: Int -> CreateEvaluation -> ShowS # show :: CreateEvaluation -> String # showList :: [CreateEvaluation] -> ShowS #
Generic CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Associated Types type Rep CreateEvaluation :: Type -> Type # Methods from :: CreateEvaluation -> Rep CreateEvaluation x # to :: Rep CreateEvaluation x -> CreateEvaluation #
NFData CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods rnf :: CreateEvaluation -> () #
Hashable CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods hashWithSalt :: Int -> CreateEvaluation -> Int # hash :: CreateEvaluation -> Int #
ToJSON CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods toJSON :: CreateEvaluation -> Value # toEncoding :: CreateEvaluation -> Encoding # toJSONList :: [CreateEvaluation] -> Value # toEncodingList :: [CreateEvaluation] -> Encoding #
AWSRequest CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Associated Types type AWSResponse CreateEvaluation # Methods request :: CreateEvaluation -> Request CreateEvaluation # response :: MonadResource m => Logger -> Service -> Proxy CreateEvaluation -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateEvaluation))) #
ToHeaders CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods toHeaders :: CreateEvaluation -> [Header] #
ToPath CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods toPath :: CreateEvaluation -> ByteString #
ToQuery CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods toQuery :: CreateEvaluation -> QueryString #
type Rep CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation type Rep CreateEvaluation = D1 ('MetaData "CreateEvaluation" "Amazonka.MachineLearning.CreateEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateEvaluation'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "evaluationName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)) :: (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "evaluationDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text))))
type AWSResponse CreateEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation type AWSResponse CreateEvaluation = CreateEvaluationResponse

newCreateEvaluation Source #

Arguments

:: Text	`$sel:evaluationId:CreateEvaluation'`
-> Text	`$sel:mLModelId:CreateEvaluation'`
-> Text	`$sel:evaluationDataSourceId:CreateEvaluation'`
-> CreateEvaluation

Create a value of CreateEvaluation with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:evaluationName:CreateEvaluation', createEvaluation_evaluationName - A user-supplied name or description of the Evaluation.

$sel:evaluationId:CreateEvaluation', createEvaluation_evaluationId - A user-supplied ID that uniquely identifies the Evaluation.

$sel:mLModelId:CreateEvaluation', createEvaluation_mLModelId - The ID of the MLModel to evaluate.

The schema used in creating the MLModel must match the schema of the DataSource used in the Evaluation.

$sel:evaluationDataSourceId:CreateEvaluation', createEvaluation_evaluationDataSourceId - The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.

data CreateEvaluationResponse Source #

Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.

CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvcaluation operation and checking the Status parameter.

See: newCreateEvaluationResponse smart constructor.

Constructors

CreateEvaluationResponse' (Maybe Text) Int

Instances

Instances details

Eq CreateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods (==) :: CreateEvaluationResponse -> CreateEvaluationResponse -> Bool # (/=) :: CreateEvaluationResponse -> CreateEvaluationResponse -> Bool #
Read CreateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods readsPrec :: Int -> ReadS CreateEvaluationResponse # readList :: ReadS [CreateEvaluationResponse] # readPrec :: ReadPrec CreateEvaluationResponse # readListPrec :: ReadPrec [CreateEvaluationResponse] #
Show CreateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods showsPrec :: Int -> CreateEvaluationResponse -> ShowS # show :: CreateEvaluationResponse -> String # showList :: [CreateEvaluationResponse] -> ShowS #
Generic CreateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Associated Types type Rep CreateEvaluationResponse :: Type -> Type # Methods from :: CreateEvaluationResponse -> Rep CreateEvaluationResponse x # to :: Rep CreateEvaluationResponse x -> CreateEvaluationResponse #
NFData CreateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation Methods rnf :: CreateEvaluationResponse -> () #
type Rep CreateEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateEvaluation type Rep CreateEvaluationResponse = D1 ('MetaData "CreateEvaluationResponse" "Amazonka.MachineLearning.CreateEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateEvaluationResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newCreateEvaluationResponse Source #

Arguments

:: Int	`$sel:httpStatus:CreateEvaluationResponse'`
-> CreateEvaluationResponse

Create a value of CreateEvaluationResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:evaluationId:CreateEvaluation', createEvaluationResponse_evaluationId - The user-supplied ID that uniquely identifies the Evaluation. This value should be identical to the value of the EvaluationId in the request.

$sel:httpStatus:CreateEvaluationResponse', createEvaluationResponse_httpStatus - The response's http status code.

Predict

data Predict Source #

See: newPredict smart constructor.

Constructors

Predict' Text (HashMap Text Text) Text

Instances

Instances details

Eq Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods (==) :: Predict -> Predict -> Bool # (/=) :: Predict -> Predict -> Bool #
Read Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods readsPrec :: Int -> ReadS Predict # readList :: ReadS [Predict] # readPrec :: ReadPrec Predict # readListPrec :: ReadPrec [Predict] #
Show Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods showsPrec :: Int -> Predict -> ShowS # show :: Predict -> String # showList :: [Predict] -> ShowS #
Generic Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Associated Types type Rep Predict :: Type -> Type # Methods from :: Predict -> Rep Predict x # to :: Rep Predict x -> Predict #
NFData Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods rnf :: Predict -> () #
Hashable Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods hashWithSalt :: Int -> Predict -> Int # hash :: Predict -> Int #
ToJSON Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods toJSON :: Predict -> Value # toEncoding :: Predict -> Encoding # toJSONList :: [Predict] -> Value # toEncodingList :: [Predict] -> Encoding #
AWSRequest Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Associated Types type AWSResponse Predict # Methods request :: Predict -> Request Predict # response :: MonadResource m => Logger -> Service -> Proxy Predict -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse Predict))) #
ToHeaders Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods toHeaders :: Predict -> [Header] #
ToPath Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods toPath :: Predict -> ByteString #
ToQuery Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods toQuery :: Predict -> QueryString #
type Rep Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict type Rep Predict = D1 ('MetaData "Predict" "Amazonka.MachineLearning.Predict" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "Predict'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: (S1 ('MetaSel ('Just "record") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (HashMap Text Text)) :: S1 ('MetaSel ('Just "predictEndpoint") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text))))
type AWSResponse Predict Source #
Instance details Defined in Amazonka.MachineLearning.Predict type AWSResponse Predict = PredictResponse

newPredict Source #

Arguments

:: Text	`$sel:mLModelId:Predict'`
-> Text	`$sel:predictEndpoint:Predict'`
-> Predict

Create a value of Predict with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelId:Predict', predict_mLModelId - A unique identifier of the MLModel.

$sel:record:Predict', predict_record - Undocumented member.

$sel:predictEndpoint:Predict', predict_predictEndpoint - Undocumented member.

data PredictResponse Source #

See: newPredictResponse smart constructor.

Constructors

PredictResponse' (Maybe Prediction) Int

Instances

Instances details

Eq PredictResponse Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods (==) :: PredictResponse -> PredictResponse -> Bool # (/=) :: PredictResponse -> PredictResponse -> Bool #
Read PredictResponse Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods readsPrec :: Int -> ReadS PredictResponse # readList :: ReadS [PredictResponse] # readPrec :: ReadPrec PredictResponse # readListPrec :: ReadPrec [PredictResponse] #
Show PredictResponse Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods showsPrec :: Int -> PredictResponse -> ShowS # show :: PredictResponse -> String # showList :: [PredictResponse] -> ShowS #
Generic PredictResponse Source #
Instance details Defined in Amazonka.MachineLearning.Predict Associated Types type Rep PredictResponse :: Type -> Type # Methods from :: PredictResponse -> Rep PredictResponse x # to :: Rep PredictResponse x -> PredictResponse #
NFData PredictResponse Source #
Instance details Defined in Amazonka.MachineLearning.Predict Methods rnf :: PredictResponse -> () #
type Rep PredictResponse Source #
Instance details Defined in Amazonka.MachineLearning.Predict type Rep PredictResponse = D1 ('MetaData "PredictResponse" "Amazonka.MachineLearning.Predict" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "PredictResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "prediction") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Prediction)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newPredictResponse Source #

Arguments

:: Int	`$sel:httpStatus:PredictResponse'`
-> PredictResponse

Create a value of PredictResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:prediction:PredictResponse', predictResponse_prediction - Undocumented member.

$sel:httpStatus:PredictResponse', predictResponse_httpStatus - The response's http status code.

DeleteRealtimeEndpoint

data DeleteRealtimeEndpoint Source #

See: newDeleteRealtimeEndpoint smart constructor.

Constructors

DeleteRealtimeEndpoint' Text

Instances

Instances details

Eq DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods (==) :: DeleteRealtimeEndpoint -> DeleteRealtimeEndpoint -> Bool # (/=) :: DeleteRealtimeEndpoint -> DeleteRealtimeEndpoint -> Bool #
Read DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods readsPrec :: Int -> ReadS DeleteRealtimeEndpoint # readList :: ReadS [DeleteRealtimeEndpoint] # readPrec :: ReadPrec DeleteRealtimeEndpoint # readListPrec :: ReadPrec [DeleteRealtimeEndpoint] #
Show DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods showsPrec :: Int -> DeleteRealtimeEndpoint -> ShowS # show :: DeleteRealtimeEndpoint -> String # showList :: [DeleteRealtimeEndpoint] -> ShowS #
Generic DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Associated Types type Rep DeleteRealtimeEndpoint :: Type -> Type # Methods from :: DeleteRealtimeEndpoint -> Rep DeleteRealtimeEndpoint x # to :: Rep DeleteRealtimeEndpoint x -> DeleteRealtimeEndpoint #
NFData DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods rnf :: DeleteRealtimeEndpoint -> () #
Hashable DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods hashWithSalt :: Int -> DeleteRealtimeEndpoint -> Int # hash :: DeleteRealtimeEndpoint -> Int #
ToJSON DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods toJSON :: DeleteRealtimeEndpoint -> Value # toEncoding :: DeleteRealtimeEndpoint -> Encoding # toJSONList :: [DeleteRealtimeEndpoint] -> Value # toEncodingList :: [DeleteRealtimeEndpoint] -> Encoding #
AWSRequest DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Associated Types type AWSResponse DeleteRealtimeEndpoint # Methods request :: DeleteRealtimeEndpoint -> Request DeleteRealtimeEndpoint # response :: MonadResource m => Logger -> Service -> Proxy DeleteRealtimeEndpoint -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DeleteRealtimeEndpoint))) #
ToHeaders DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods toHeaders :: DeleteRealtimeEndpoint -> [Header] #
ToPath DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods toPath :: DeleteRealtimeEndpoint -> ByteString #
ToQuery DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods toQuery :: DeleteRealtimeEndpoint -> QueryString #
type Rep DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint type Rep DeleteRealtimeEndpoint = D1 ('MetaData "DeleteRealtimeEndpoint" "Amazonka.MachineLearning.DeleteRealtimeEndpoint" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteRealtimeEndpoint'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse DeleteRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint type AWSResponse DeleteRealtimeEndpoint = DeleteRealtimeEndpointResponse

newDeleteRealtimeEndpoint Source #

Arguments

:: Text	`$sel:mLModelId:DeleteRealtimeEndpoint'`
-> DeleteRealtimeEndpoint

Create a value of DeleteRealtimeEndpoint with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelId:DeleteRealtimeEndpoint', deleteRealtimeEndpoint_mLModelId - The ID assigned to the MLModel during creation.

data DeleteRealtimeEndpointResponse Source #

Represents the output of an DeleteRealtimeEndpoint operation.

The result contains the MLModelId and the endpoint information for the MLModel.

See: newDeleteRealtimeEndpointResponse smart constructor.

Constructors

DeleteRealtimeEndpointResponse' (Maybe RealtimeEndpointInfo) (Maybe Text) Int

Instances

Instances details

Eq DeleteRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods (==) :: DeleteRealtimeEndpointResponse -> DeleteRealtimeEndpointResponse -> Bool # (/=) :: DeleteRealtimeEndpointResponse -> DeleteRealtimeEndpointResponse -> Bool #
Read DeleteRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods readsPrec :: Int -> ReadS DeleteRealtimeEndpointResponse # readList :: ReadS [DeleteRealtimeEndpointResponse] # readPrec :: ReadPrec DeleteRealtimeEndpointResponse # readListPrec :: ReadPrec [DeleteRealtimeEndpointResponse] #
Show DeleteRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods showsPrec :: Int -> DeleteRealtimeEndpointResponse -> ShowS # show :: DeleteRealtimeEndpointResponse -> String # showList :: [DeleteRealtimeEndpointResponse] -> ShowS #
Generic DeleteRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Associated Types type Rep DeleteRealtimeEndpointResponse :: Type -> Type # Methods from :: DeleteRealtimeEndpointResponse -> Rep DeleteRealtimeEndpointResponse x # to :: Rep DeleteRealtimeEndpointResponse x -> DeleteRealtimeEndpointResponse #
NFData DeleteRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint Methods rnf :: DeleteRealtimeEndpointResponse -> () #
type Rep DeleteRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.DeleteRealtimeEndpoint type Rep DeleteRealtimeEndpointResponse = D1 ('MetaData "DeleteRealtimeEndpointResponse" "Amazonka.MachineLearning.DeleteRealtimeEndpoint" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DeleteRealtimeEndpointResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "realtimeEndpointInfo") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RealtimeEndpointInfo)) :: (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newDeleteRealtimeEndpointResponse Source #

Arguments

:: Int	`$sel:httpStatus:DeleteRealtimeEndpointResponse'`
-> DeleteRealtimeEndpointResponse

Create a value of DeleteRealtimeEndpointResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:realtimeEndpointInfo:DeleteRealtimeEndpointResponse', deleteRealtimeEndpointResponse_realtimeEndpointInfo - The endpoint information of the MLModel

$sel:mLModelId:DeleteRealtimeEndpoint', deleteRealtimeEndpointResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

$sel:httpStatus:DeleteRealtimeEndpointResponse', deleteRealtimeEndpointResponse_httpStatus - The response's http status code.

CreateBatchPrediction

data CreateBatchPrediction Source #

See: newCreateBatchPrediction smart constructor.

Constructors

CreateBatchPrediction' (Maybe Text) Text Text Text Text

Instances

Instances details

Eq CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods (==) :: CreateBatchPrediction -> CreateBatchPrediction -> Bool # (/=) :: CreateBatchPrediction -> CreateBatchPrediction -> Bool #
Read CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods readsPrec :: Int -> ReadS CreateBatchPrediction # readList :: ReadS [CreateBatchPrediction] # readPrec :: ReadPrec CreateBatchPrediction # readListPrec :: ReadPrec [CreateBatchPrediction] #
Show CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods showsPrec :: Int -> CreateBatchPrediction -> ShowS # show :: CreateBatchPrediction -> String # showList :: [CreateBatchPrediction] -> ShowS #
Generic CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Associated Types type Rep CreateBatchPrediction :: Type -> Type # Methods from :: CreateBatchPrediction -> Rep CreateBatchPrediction x # to :: Rep CreateBatchPrediction x -> CreateBatchPrediction #
NFData CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods rnf :: CreateBatchPrediction -> () #
Hashable CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods hashWithSalt :: Int -> CreateBatchPrediction -> Int # hash :: CreateBatchPrediction -> Int #
ToJSON CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods toJSON :: CreateBatchPrediction -> Value # toEncoding :: CreateBatchPrediction -> Encoding # toJSONList :: [CreateBatchPrediction] -> Value # toEncodingList :: [CreateBatchPrediction] -> Encoding #
AWSRequest CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Associated Types type AWSResponse CreateBatchPrediction # Methods request :: CreateBatchPrediction -> Request CreateBatchPrediction # response :: MonadResource m => Logger -> Service -> Proxy CreateBatchPrediction -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateBatchPrediction))) #
ToHeaders CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods toHeaders :: CreateBatchPrediction -> [Header] #
ToPath CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods toPath :: CreateBatchPrediction -> ByteString #
ToQuery CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods toQuery :: CreateBatchPrediction -> QueryString #
type Rep CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction type Rep CreateBatchPrediction = D1 ('MetaData "CreateBatchPrediction" "Amazonka.MachineLearning.CreateBatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateBatchPrediction'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "batchPredictionName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)) :: (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: (S1 ('MetaSel ('Just "batchPredictionDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: S1 ('MetaSel ('Just "outputUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))))
type AWSResponse CreateBatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction type AWSResponse CreateBatchPrediction = CreateBatchPredictionResponse

newCreateBatchPrediction Source #

Arguments

:: Text	`$sel:batchPredictionId:CreateBatchPrediction'`
-> Text	`$sel:mLModelId:CreateBatchPrediction'`
-> Text	`$sel:batchPredictionDataSourceId:CreateBatchPrediction'`
-> Text	`$sel:outputUri:CreateBatchPrediction'`
-> CreateBatchPrediction

Create a value of CreateBatchPrediction with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:batchPredictionName:CreateBatchPrediction', createBatchPrediction_batchPredictionName - A user-supplied name or description of the BatchPrediction. BatchPredictionName can only use the UTF-8 character set.

$sel:batchPredictionId:CreateBatchPrediction', createBatchPrediction_batchPredictionId - A user-supplied ID that uniquely identifies the BatchPrediction.

$sel:mLModelId:CreateBatchPrediction', createBatchPrediction_mLModelId - The ID of the MLModel that will generate predictions for the group of observations.

$sel:batchPredictionDataSourceId:CreateBatchPrediction', createBatchPrediction_batchPredictionDataSourceId - The ID of the DataSource that points to the group of observations to predict.

$sel:outputUri:CreateBatchPrediction', createBatchPrediction_outputUri - The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'.

Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the Amazon Machine Learning Developer Guide.

data CreateBatchPredictionResponse Source #

Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.

The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the >GetBatchPrediction operation and checking the Status parameter of the result.

See: newCreateBatchPredictionResponse smart constructor.

Constructors

CreateBatchPredictionResponse' (Maybe Text) Int

Instances

Instances details

Eq CreateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods (==) :: CreateBatchPredictionResponse -> CreateBatchPredictionResponse -> Bool # (/=) :: CreateBatchPredictionResponse -> CreateBatchPredictionResponse -> Bool #
Read CreateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods readsPrec :: Int -> ReadS CreateBatchPredictionResponse # readList :: ReadS [CreateBatchPredictionResponse] # readPrec :: ReadPrec CreateBatchPredictionResponse # readListPrec :: ReadPrec [CreateBatchPredictionResponse] #
Show CreateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods showsPrec :: Int -> CreateBatchPredictionResponse -> ShowS # show :: CreateBatchPredictionResponse -> String # showList :: [CreateBatchPredictionResponse] -> ShowS #
Generic CreateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Associated Types type Rep CreateBatchPredictionResponse :: Type -> Type # Methods from :: CreateBatchPredictionResponse -> Rep CreateBatchPredictionResponse x # to :: Rep CreateBatchPredictionResponse x -> CreateBatchPredictionResponse #
NFData CreateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction Methods rnf :: CreateBatchPredictionResponse -> () #
type Rep CreateBatchPredictionResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateBatchPrediction type Rep CreateBatchPredictionResponse = D1 ('MetaData "CreateBatchPredictionResponse" "Amazonka.MachineLearning.CreateBatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateBatchPredictionResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int)))

newCreateBatchPredictionResponse Source #

Arguments

:: Int	`$sel:httpStatus:CreateBatchPredictionResponse'`
-> CreateBatchPredictionResponse

Create a value of CreateBatchPredictionResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:batchPredictionId:CreateBatchPrediction', createBatchPredictionResponse_batchPredictionId - A user-supplied ID that uniquely identifies the BatchPrediction. This value is identical to the value of the BatchPredictionId in the request.

$sel:httpStatus:CreateBatchPredictionResponse', createBatchPredictionResponse_httpStatus - The response's http status code.

GetEvaluation

data GetEvaluation Source #

See: newGetEvaluation smart constructor.

Constructors

GetEvaluation' Text

Instances

Instances details

Eq GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods (==) :: GetEvaluation -> GetEvaluation -> Bool # (/=) :: GetEvaluation -> GetEvaluation -> Bool #
Read GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods readsPrec :: Int -> ReadS GetEvaluation # readList :: ReadS [GetEvaluation] # readPrec :: ReadPrec GetEvaluation # readListPrec :: ReadPrec [GetEvaluation] #
Show GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods showsPrec :: Int -> GetEvaluation -> ShowS # show :: GetEvaluation -> String # showList :: [GetEvaluation] -> ShowS #
Generic GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Associated Types type Rep GetEvaluation :: Type -> Type # Methods from :: GetEvaluation -> Rep GetEvaluation x # to :: Rep GetEvaluation x -> GetEvaluation #
NFData GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods rnf :: GetEvaluation -> () #
Hashable GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods hashWithSalt :: Int -> GetEvaluation -> Int # hash :: GetEvaluation -> Int #
ToJSON GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods toJSON :: GetEvaluation -> Value # toEncoding :: GetEvaluation -> Encoding # toJSONList :: [GetEvaluation] -> Value # toEncodingList :: [GetEvaluation] -> Encoding #
AWSRequest GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Associated Types type AWSResponse GetEvaluation # Methods request :: GetEvaluation -> Request GetEvaluation # response :: MonadResource m => Logger -> Service -> Proxy GetEvaluation -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse GetEvaluation))) #
ToHeaders GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods toHeaders :: GetEvaluation -> [Header] #
ToPath GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods toPath :: GetEvaluation -> ByteString #
ToQuery GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods toQuery :: GetEvaluation -> QueryString #
type Rep GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation type Rep GetEvaluation = D1 ('MetaData "GetEvaluation" "Amazonka.MachineLearning.GetEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "GetEvaluation'" 'PrefixI 'True) (S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse GetEvaluation Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation type AWSResponse GetEvaluation = GetEvaluationResponse

newGetEvaluation Source #

Arguments

:: Text	`$sel:evaluationId:GetEvaluation'`
-> GetEvaluation

Create a value of GetEvaluation with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:evaluationId:GetEvaluation', getEvaluation_evaluationId - The ID of the Evaluation to retrieve. The evaluation of each MLModel is recorded and cataloged. The ID provides the means to access the information.

data GetEvaluationResponse Source #

Represents the output of a GetEvaluation operation and describes an Evaluation.

See: newGetEvaluationResponse smart constructor.

Constructors

GetEvaluationResponse' (Maybe EntityStatus) (Maybe PerformanceMetrics) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) Int

Instances

Instances details

Eq GetEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods (==) :: GetEvaluationResponse -> GetEvaluationResponse -> Bool # (/=) :: GetEvaluationResponse -> GetEvaluationResponse -> Bool #
Read GetEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods readsPrec :: Int -> ReadS GetEvaluationResponse # readList :: ReadS [GetEvaluationResponse] # readPrec :: ReadPrec GetEvaluationResponse # readListPrec :: ReadPrec [GetEvaluationResponse] #
Show GetEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods showsPrec :: Int -> GetEvaluationResponse -> ShowS # show :: GetEvaluationResponse -> String # showList :: [GetEvaluationResponse] -> ShowS #
Generic GetEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Associated Types type Rep GetEvaluationResponse :: Type -> Type # Methods from :: GetEvaluationResponse -> Rep GetEvaluationResponse x # to :: Rep GetEvaluationResponse x -> GetEvaluationResponse #
NFData GetEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation Methods rnf :: GetEvaluationResponse -> () #
type Rep GetEvaluationResponse Source #
Instance details Defined in Amazonka.MachineLearning.GetEvaluation type Rep GetEvaluationResponse = D1 ('MetaData "GetEvaluationResponse" "Amazonka.MachineLearning.GetEvaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "GetEvaluationResponse'" 'PrefixI 'True) ((((S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EntityStatus)) :: S1 ('MetaSel ('Just "performanceMetrics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe PerformanceMetrics))) :: (S1 ('MetaSel ('Just "lastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)))) :: ((S1 ('MetaSel ('Just "computeTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: S1 ('MetaSel ('Just "inputDataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "startedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))))) :: (((S1 ('MetaSel ('Just "finishedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "createdByIamUser") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "name") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "logUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :: ((S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "evaluationDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))))

newGetEvaluationResponse Source #

Arguments

:: Int	`$sel:httpStatus:GetEvaluationResponse'`
-> GetEvaluationResponse

Create a value of GetEvaluationResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:status:GetEvaluationResponse', getEvaluationResponse_status - The status of the evaluation. This element can have one of the following values:

PENDING - Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel.
INPROGRESS - The evaluation is underway.
FAILED - The request to evaluate an MLModel did not run to completion. It is not usable.
COMPLETED - The evaluation process completed successfully.
DELETED - The Evaluation is marked as deleted. It is not usable.

$sel:performanceMetrics:GetEvaluationResponse', getEvaluationResponse_performanceMetrics - Measurements of how well the MLModel performed using observations referenced by the DataSource. One of the following metric is returned based on the type of the MLModel:

BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

$sel:lastUpdatedAt:GetEvaluationResponse', getEvaluationResponse_lastUpdatedAt - The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

$sel:createdAt:GetEvaluationResponse', getEvaluationResponse_createdAt - The time that the Evaluation was created. The time is expressed in epoch time.

$sel:computeTime:GetEvaluationResponse', getEvaluationResponse_computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the Evaluation, normalized and scaled on computation resources. ComputeTime is only available if the Evaluation is in the COMPLETED state.

$sel:inputDataLocationS3:GetEvaluationResponse', getEvaluationResponse_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

$sel:mLModelId:GetEvaluationResponse', getEvaluationResponse_mLModelId - The ID of the MLModel that was the focus of the evaluation.

$sel:startedAt:GetEvaluationResponse', getEvaluationResponse_startedAt - The epoch time when Amazon Machine Learning marked the Evaluation as INPROGRESS. StartedAt isn't available if the Evaluation is in the PENDING state.

$sel:finishedAt:GetEvaluationResponse', getEvaluationResponse_finishedAt - The epoch time when Amazon Machine Learning marked the Evaluation as COMPLETED or FAILED. FinishedAt is only available when the Evaluation is in the COMPLETED or FAILED state.

$sel:createdByIamUser:GetEvaluationResponse', getEvaluationResponse_createdByIamUser - The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

$sel:name:GetEvaluationResponse', getEvaluationResponse_name - A user-supplied name or description of the Evaluation.

$sel:logUri:GetEvaluationResponse', getEvaluationResponse_logUri - A link to the file that contains logs of the CreateEvaluation operation.

$sel:evaluationId:GetEvaluation', getEvaluationResponse_evaluationId - The evaluation ID which is same as the EvaluationId in the request.

$sel:message:GetEvaluationResponse', getEvaluationResponse_message - A description of the most recent details about evaluating the MLModel.

$sel:evaluationDataSourceId:GetEvaluationResponse', getEvaluationResponse_evaluationDataSourceId - The DataSource used for this evaluation.

$sel:httpStatus:GetEvaluationResponse', getEvaluationResponse_httpStatus - The response's http status code.

DescribeEvaluations (Paginated)

data DescribeEvaluations Source #

See: newDescribeEvaluations smart constructor.

Constructors

DescribeEvaluations' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) (Maybe Natural) (Maybe Text) (Maybe EvaluationFilterVariable) (Maybe Text)

Instances

Instances details

Eq DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods (==) :: DescribeEvaluations -> DescribeEvaluations -> Bool # (/=) :: DescribeEvaluations -> DescribeEvaluations -> Bool #
Read DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods readsPrec :: Int -> ReadS DescribeEvaluations # readList :: ReadS [DescribeEvaluations] # readPrec :: ReadPrec DescribeEvaluations # readListPrec :: ReadPrec [DescribeEvaluations] #
Show DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods showsPrec :: Int -> DescribeEvaluations -> ShowS # show :: DescribeEvaluations -> String # showList :: [DescribeEvaluations] -> ShowS #
Generic DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Associated Types type Rep DescribeEvaluations :: Type -> Type # Methods from :: DescribeEvaluations -> Rep DescribeEvaluations x # to :: Rep DescribeEvaluations x -> DescribeEvaluations #
NFData DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods rnf :: DescribeEvaluations -> () #
Hashable DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods hashWithSalt :: Int -> DescribeEvaluations -> Int # hash :: DescribeEvaluations -> Int #
ToJSON DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods toJSON :: DescribeEvaluations -> Value # toEncoding :: DescribeEvaluations -> Encoding # toJSONList :: [DescribeEvaluations] -> Value # toEncodingList :: [DescribeEvaluations] -> Encoding #
AWSPager DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods page :: DescribeEvaluations -> AWSResponse DescribeEvaluations -> Maybe DescribeEvaluations #
AWSRequest DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Associated Types type AWSResponse DescribeEvaluations # Methods request :: DescribeEvaluations -> Request DescribeEvaluations # response :: MonadResource m => Logger -> Service -> Proxy DescribeEvaluations -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DescribeEvaluations))) #
ToHeaders DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods toHeaders :: DescribeEvaluations -> [Header] #
ToPath DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods toPath :: DescribeEvaluations -> ByteString #
ToQuery DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods toQuery :: DescribeEvaluations -> QueryString #
type Rep DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations type Rep DescribeEvaluations = D1 ('MetaData "DescribeEvaluations" "Amazonka.MachineLearning.DescribeEvaluations" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeEvaluations'" 'PrefixI 'True) (((S1 ('MetaSel ('Just "eq") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "ge") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "prefix") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "gt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "ne") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))) :: ((S1 ('MetaSel ('Just "nextToken") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "sortOrder") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe SortOrder)) :: S1 ('MetaSel ('Just "limit") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Natural)))) :: (S1 ('MetaSel ('Just "lt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "filterVariable") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EvaluationFilterVariable)) :: S1 ('MetaSel ('Just "le") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))))
type AWSResponse DescribeEvaluations Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations type AWSResponse DescribeEvaluations = DescribeEvaluationsResponse

newDescribeEvaluations :: DescribeEvaluations Source #

Create a value of DescribeEvaluations with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:eq:DescribeEvaluations', describeEvaluations_eq - The equal to operator. The Evaluation results will have FilterVariable values that exactly match the value specified with EQ.

$sel:ge:DescribeEvaluations', describeEvaluations_ge - The greater than or equal to operator. The Evaluation results will have FilterVariable values that are greater than or equal to the value specified with GE.

$sel:prefix:DescribeEvaluations', describeEvaluations_prefix - A string that is found at the beginning of a variable, such as Name or Id.

For example, an Evaluation could have the Name 2014-09-09-HolidayGiftMailer. To search for this Evaluation, select Name for the FilterVariable and any of the following strings for the Prefix:

2014-09
2014-09-09
2014-09-09-Holiday

$sel:gt:DescribeEvaluations', describeEvaluations_gt - The greater than operator. The Evaluation results will have FilterVariable values that are greater than the value specified with GT.

$sel:ne:DescribeEvaluations', describeEvaluations_ne - The not equal to operator. The Evaluation results will have FilterVariable values not equal to the value specified with NE.

$sel:nextToken:DescribeEvaluations', describeEvaluations_nextToken - The ID of the page in the paginated results.

$sel:sortOrder:DescribeEvaluations', describeEvaluations_sortOrder - A two-value parameter that determines the sequence of the resulting list of Evaluation.

asc - Arranges the list in ascending order (A-Z, 0-9).
dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

$sel:limit:DescribeEvaluations', describeEvaluations_limit - The maximum number of Evaluation to include in the result.

$sel:lt:DescribeEvaluations', describeEvaluations_lt - The less than operator. The Evaluation results will have FilterVariable values that are less than the value specified with LT.

$sel:filterVariable:DescribeEvaluations', describeEvaluations_filterVariable - Use one of the following variable to filter a list of Evaluation objects:

CreatedAt - Sets the search criteria to the Evaluation creation date.
Status - Sets the search criteria to the Evaluation status.
Name - Sets the search criteria to the contents of Evaluation ____ Name.
IAMUser - Sets the search criteria to the user account that invoked an Evaluation.
MLModelId - Sets the search criteria to the MLModel that was evaluated.
DataSourceId - Sets the search criteria to the DataSource used in Evaluation.
DataUri - Sets the search criteria to the data file(s) used in Evaluation. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.

$sel:le:DescribeEvaluations', describeEvaluations_le - The less than or equal to operator. The Evaluation results will have FilterVariable values that are less than or equal to the value specified with LE.

data DescribeEvaluationsResponse Source #

Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.

See: newDescribeEvaluationsResponse smart constructor.

Constructors

DescribeEvaluationsResponse' (Maybe [Evaluation]) (Maybe Text) Int

Instances

Instances details

Eq DescribeEvaluationsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods (==) :: DescribeEvaluationsResponse -> DescribeEvaluationsResponse -> Bool # (/=) :: DescribeEvaluationsResponse -> DescribeEvaluationsResponse -> Bool #
Read DescribeEvaluationsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods readsPrec :: Int -> ReadS DescribeEvaluationsResponse # readList :: ReadS [DescribeEvaluationsResponse] # readPrec :: ReadPrec DescribeEvaluationsResponse # readListPrec :: ReadPrec [DescribeEvaluationsResponse] #
Show DescribeEvaluationsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods showsPrec :: Int -> DescribeEvaluationsResponse -> ShowS # show :: DescribeEvaluationsResponse -> String # showList :: [DescribeEvaluationsResponse] -> ShowS #
Generic DescribeEvaluationsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Associated Types type Rep DescribeEvaluationsResponse :: Type -> Type # Methods from :: DescribeEvaluationsResponse -> Rep DescribeEvaluationsResponse x # to :: Rep DescribeEvaluationsResponse x -> DescribeEvaluationsResponse #
NFData DescribeEvaluationsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations Methods rnf :: DescribeEvaluationsResponse -> () #
type Rep DescribeEvaluationsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeEvaluations type Rep DescribeEvaluationsResponse = D1 ('MetaData "DescribeEvaluationsResponse" "Amazonka.MachineLearning.DescribeEvaluations" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeEvaluationsResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "results") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [Evaluation])) :: (S1 ('MetaSel ('Just "nextToken") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newDescribeEvaluationsResponse Source #

Arguments

:: Int	`$sel:httpStatus:DescribeEvaluationsResponse'`
-> DescribeEvaluationsResponse

Create a value of DescribeEvaluationsResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:results:DescribeEvaluationsResponse', describeEvaluationsResponse_results - A list of Evaluation that meet the search criteria.

$sel:nextToken:DescribeEvaluations', describeEvaluationsResponse_nextToken - The ID of the next page in the paginated results that indicates at least one more page follows.

$sel:httpStatus:DescribeEvaluationsResponse', describeEvaluationsResponse_httpStatus - The response's http status code.

CreateRealtimeEndpoint

data CreateRealtimeEndpoint Source #

See: newCreateRealtimeEndpoint smart constructor.

Constructors

CreateRealtimeEndpoint' Text

Instances

Instances details

Eq CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods (==) :: CreateRealtimeEndpoint -> CreateRealtimeEndpoint -> Bool # (/=) :: CreateRealtimeEndpoint -> CreateRealtimeEndpoint -> Bool #
Read CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods readsPrec :: Int -> ReadS CreateRealtimeEndpoint # readList :: ReadS [CreateRealtimeEndpoint] # readPrec :: ReadPrec CreateRealtimeEndpoint # readListPrec :: ReadPrec [CreateRealtimeEndpoint] #
Show CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods showsPrec :: Int -> CreateRealtimeEndpoint -> ShowS # show :: CreateRealtimeEndpoint -> String # showList :: [CreateRealtimeEndpoint] -> ShowS #
Generic CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Associated Types type Rep CreateRealtimeEndpoint :: Type -> Type # Methods from :: CreateRealtimeEndpoint -> Rep CreateRealtimeEndpoint x # to :: Rep CreateRealtimeEndpoint x -> CreateRealtimeEndpoint #
NFData CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods rnf :: CreateRealtimeEndpoint -> () #
Hashable CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods hashWithSalt :: Int -> CreateRealtimeEndpoint -> Int # hash :: CreateRealtimeEndpoint -> Int #
ToJSON CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods toJSON :: CreateRealtimeEndpoint -> Value # toEncoding :: CreateRealtimeEndpoint -> Encoding # toJSONList :: [CreateRealtimeEndpoint] -> Value # toEncodingList :: [CreateRealtimeEndpoint] -> Encoding #
AWSRequest CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Associated Types type AWSResponse CreateRealtimeEndpoint # Methods request :: CreateRealtimeEndpoint -> Request CreateRealtimeEndpoint # response :: MonadResource m => Logger -> Service -> Proxy CreateRealtimeEndpoint -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateRealtimeEndpoint))) #
ToHeaders CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods toHeaders :: CreateRealtimeEndpoint -> [Header] #
ToPath CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods toPath :: CreateRealtimeEndpoint -> ByteString #
ToQuery CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods toQuery :: CreateRealtimeEndpoint -> QueryString #
type Rep CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint type Rep CreateRealtimeEndpoint = D1 ('MetaData "CreateRealtimeEndpoint" "Amazonka.MachineLearning.CreateRealtimeEndpoint" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateRealtimeEndpoint'" 'PrefixI 'True) (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))
type AWSResponse CreateRealtimeEndpoint Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint type AWSResponse CreateRealtimeEndpoint = CreateRealtimeEndpointResponse

newCreateRealtimeEndpoint Source #

Arguments

:: Text	`$sel:mLModelId:CreateRealtimeEndpoint'`
-> CreateRealtimeEndpoint

Create a value of CreateRealtimeEndpoint with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:mLModelId:CreateRealtimeEndpoint', createRealtimeEndpoint_mLModelId - The ID assigned to the MLModel during creation.

data CreateRealtimeEndpointResponse Source #

Represents the output of an CreateRealtimeEndpoint operation.

The result contains the MLModelId and the endpoint information for the MLModel.

Note: The endpoint information includes the URI of the MLModel; that is, the location to send online prediction requests for the specified MLModel.

See: newCreateRealtimeEndpointResponse smart constructor.

Constructors

CreateRealtimeEndpointResponse' (Maybe RealtimeEndpointInfo) (Maybe Text) Int

Instances

Instances details

Eq CreateRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods (==) :: CreateRealtimeEndpointResponse -> CreateRealtimeEndpointResponse -> Bool # (/=) :: CreateRealtimeEndpointResponse -> CreateRealtimeEndpointResponse -> Bool #
Read CreateRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods readsPrec :: Int -> ReadS CreateRealtimeEndpointResponse # readList :: ReadS [CreateRealtimeEndpointResponse] # readPrec :: ReadPrec CreateRealtimeEndpointResponse # readListPrec :: ReadPrec [CreateRealtimeEndpointResponse] #
Show CreateRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods showsPrec :: Int -> CreateRealtimeEndpointResponse -> ShowS # show :: CreateRealtimeEndpointResponse -> String # showList :: [CreateRealtimeEndpointResponse] -> ShowS #
Generic CreateRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Associated Types type Rep CreateRealtimeEndpointResponse :: Type -> Type # Methods from :: CreateRealtimeEndpointResponse -> Rep CreateRealtimeEndpointResponse x # to :: Rep CreateRealtimeEndpointResponse x -> CreateRealtimeEndpointResponse #
NFData CreateRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint Methods rnf :: CreateRealtimeEndpointResponse -> () #
type Rep CreateRealtimeEndpointResponse Source #
Instance details Defined in Amazonka.MachineLearning.CreateRealtimeEndpoint type Rep CreateRealtimeEndpointResponse = D1 ('MetaData "CreateRealtimeEndpointResponse" "Amazonka.MachineLearning.CreateRealtimeEndpoint" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "CreateRealtimeEndpointResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "realtimeEndpointInfo") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RealtimeEndpointInfo)) :: (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newCreateRealtimeEndpointResponse Source #

Arguments

:: Int	`$sel:httpStatus:CreateRealtimeEndpointResponse'`
-> CreateRealtimeEndpointResponse

Create a value of CreateRealtimeEndpointResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:realtimeEndpointInfo:CreateRealtimeEndpointResponse', createRealtimeEndpointResponse_realtimeEndpointInfo - The endpoint information of the MLModel

$sel:mLModelId:CreateRealtimeEndpoint', createRealtimeEndpointResponse_mLModelId - A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.

$sel:httpStatus:CreateRealtimeEndpointResponse', createRealtimeEndpointResponse_httpStatus - The response's http status code.

AddTags

data AddTags Source #

See: newAddTags smart constructor.

Constructors

AddTags' [Tag] Text TaggableResourceType

Instances

Instances details

Eq AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods (==) :: AddTags -> AddTags -> Bool # (/=) :: AddTags -> AddTags -> Bool #
Read AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods readsPrec :: Int -> ReadS AddTags # readList :: ReadS [AddTags] # readPrec :: ReadPrec AddTags # readListPrec :: ReadPrec [AddTags] #
Show AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods showsPrec :: Int -> AddTags -> ShowS # show :: AddTags -> String # showList :: [AddTags] -> ShowS #
Generic AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Associated Types type Rep AddTags :: Type -> Type # Methods from :: AddTags -> Rep AddTags x # to :: Rep AddTags x -> AddTags #
NFData AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods rnf :: AddTags -> () #
Hashable AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods hashWithSalt :: Int -> AddTags -> Int # hash :: AddTags -> Int #
ToJSON AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods toJSON :: AddTags -> Value # toEncoding :: AddTags -> Encoding # toJSONList :: [AddTags] -> Value # toEncodingList :: [AddTags] -> Encoding #
AWSRequest AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Associated Types type AWSResponse AddTags # Methods request :: AddTags -> Request AddTags # response :: MonadResource m => Logger -> Service -> Proxy AddTags -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse AddTags))) #
ToHeaders AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods toHeaders :: AddTags -> [Header] #
ToPath AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods toPath :: AddTags -> ByteString #
ToQuery AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods toQuery :: AddTags -> QueryString #
type Rep AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags type Rep AddTags = D1 ('MetaData "AddTags" "Amazonka.MachineLearning.AddTags" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "AddTags'" 'PrefixI 'True) (S1 ('MetaSel ('Just "tags") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 [Tag]) :: (S1 ('MetaSel ('Just "resourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: S1 ('MetaSel ('Just "resourceType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 TaggableResourceType))))
type AWSResponse AddTags Source #
Instance details Defined in Amazonka.MachineLearning.AddTags type AWSResponse AddTags = AddTagsResponse

newAddTags Source #

Arguments

:: Text	`$sel:resourceId:AddTags'`
-> TaggableResourceType	`$sel:resourceType:AddTags'`
-> AddTags

Create a value of AddTags with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:tags:AddTags', addTags_tags - The key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null.

$sel:resourceId:AddTags', addTags_resourceId - The ID of the ML object to tag. For example, exampleModelId.

$sel:resourceType:AddTags', addTags_resourceType - The type of the ML object to tag.

data AddTagsResponse Source #

Amazon ML returns the following elements.

See: newAddTagsResponse smart constructor.

Constructors

AddTagsResponse' (Maybe Text) (Maybe TaggableResourceType) Int

Instances

Instances details

Eq AddTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods (==) :: AddTagsResponse -> AddTagsResponse -> Bool # (/=) :: AddTagsResponse -> AddTagsResponse -> Bool #
Read AddTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods readsPrec :: Int -> ReadS AddTagsResponse # readList :: ReadS [AddTagsResponse] # readPrec :: ReadPrec AddTagsResponse # readListPrec :: ReadPrec [AddTagsResponse] #
Show AddTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods showsPrec :: Int -> AddTagsResponse -> ShowS # show :: AddTagsResponse -> String # showList :: [AddTagsResponse] -> ShowS #
Generic AddTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Associated Types type Rep AddTagsResponse :: Type -> Type # Methods from :: AddTagsResponse -> Rep AddTagsResponse x # to :: Rep AddTagsResponse x -> AddTagsResponse #
NFData AddTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.AddTags Methods rnf :: AddTagsResponse -> () #
type Rep AddTagsResponse Source #
Instance details Defined in Amazonka.MachineLearning.AddTags type Rep AddTagsResponse = D1 ('MetaData "AddTagsResponse" "Amazonka.MachineLearning.AddTags" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "AddTagsResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "resourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "resourceType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe TaggableResourceType)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newAddTagsResponse Source #

Arguments

:: Int	`$sel:httpStatus:AddTagsResponse'`
-> AddTagsResponse

Create a value of AddTagsResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:resourceId:AddTags', addTagsResponse_resourceId - The ID of the ML object that was tagged.

$sel:resourceType:AddTags', addTagsResponse_resourceType - The type of the ML object that was tagged.

$sel:httpStatus:AddTagsResponse', addTagsResponse_httpStatus - The response's http status code.

DescribeMLModels (Paginated)

data DescribeMLModels Source #

See: newDescribeMLModels smart constructor.

Constructors

DescribeMLModels' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) (Maybe Natural) (Maybe Text) (Maybe MLModelFilterVariable) (Maybe Text)

Instances

Instances details

Eq DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods (==) :: DescribeMLModels -> DescribeMLModels -> Bool # (/=) :: DescribeMLModels -> DescribeMLModels -> Bool #
Read DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods readsPrec :: Int -> ReadS DescribeMLModels # readList :: ReadS [DescribeMLModels] # readPrec :: ReadPrec DescribeMLModels # readListPrec :: ReadPrec [DescribeMLModels] #
Show DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods showsPrec :: Int -> DescribeMLModels -> ShowS # show :: DescribeMLModels -> String # showList :: [DescribeMLModels] -> ShowS #
Generic DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Associated Types type Rep DescribeMLModels :: Type -> Type # Methods from :: DescribeMLModels -> Rep DescribeMLModels x # to :: Rep DescribeMLModels x -> DescribeMLModels #
NFData DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods rnf :: DescribeMLModels -> () #
Hashable DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods hashWithSalt :: Int -> DescribeMLModels -> Int # hash :: DescribeMLModels -> Int #
ToJSON DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods toJSON :: DescribeMLModels -> Value # toEncoding :: DescribeMLModels -> Encoding # toJSONList :: [DescribeMLModels] -> Value # toEncodingList :: [DescribeMLModels] -> Encoding #
AWSPager DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods page :: DescribeMLModels -> AWSResponse DescribeMLModels -> Maybe DescribeMLModels #
AWSRequest DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Associated Types type AWSResponse DescribeMLModels # Methods request :: DescribeMLModels -> Request DescribeMLModels # response :: MonadResource m => Logger -> Service -> Proxy DescribeMLModels -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DescribeMLModels))) #
ToHeaders DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods toHeaders :: DescribeMLModels -> [Header] #
ToPath DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods toPath :: DescribeMLModels -> ByteString #
ToQuery DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods toQuery :: DescribeMLModels -> QueryString #
type Rep DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels type Rep DescribeMLModels = D1 ('MetaData "DescribeMLModels" "Amazonka.MachineLearning.DescribeMLModels" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeMLModels'" 'PrefixI 'True) (((S1 ('MetaSel ('Just "eq") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "ge") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "prefix") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "gt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "ne") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))) :: ((S1 ('MetaSel ('Just "nextToken") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "sortOrder") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe SortOrder)) :: S1 ('MetaSel ('Just "limit") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Natural)))) :: (S1 ('MetaSel ('Just "lt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "filterVariable") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe MLModelFilterVariable)) :: S1 ('MetaSel ('Just "le") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))))
type AWSResponse DescribeMLModels Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels type AWSResponse DescribeMLModels = DescribeMLModelsResponse

newDescribeMLModels :: DescribeMLModels Source #

Create a value of DescribeMLModels with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:eq:DescribeMLModels', describeMLModels_eq - The equal to operator. The MLModel results will have FilterVariable values that exactly match the value specified with EQ.

$sel:ge:DescribeMLModels', describeMLModels_ge - The greater than or equal to operator. The MLModel results will have FilterVariable values that are greater than or equal to the value specified with GE.

$sel:prefix:DescribeMLModels', describeMLModels_prefix - A string that is found at the beginning of a variable, such as Name or Id.

For example, an MLModel could have the Name 2014-09-09-HolidayGiftMailer. To search for this MLModel, select Name for the FilterVariable and any of the following strings for the Prefix:

2014-09
2014-09-09
2014-09-09-Holiday

$sel:gt:DescribeMLModels', describeMLModels_gt - The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT.

$sel:ne:DescribeMLModels', describeMLModels_ne - The not equal to operator. The MLModel results will have FilterVariable values not equal to the value specified with NE.

$sel:nextToken:DescribeMLModels', describeMLModels_nextToken - The ID of the page in the paginated results.

$sel:sortOrder:DescribeMLModels', describeMLModels_sortOrder - A two-value parameter that determines the sequence of the resulting list of MLModel.

asc - Arranges the list in ascending order (A-Z, 0-9).
dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

$sel:limit:DescribeMLModels', describeMLModels_limit - The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.

$sel:lt:DescribeMLModels', describeMLModels_lt - The less than operator. The MLModel results will have FilterVariable values that are less than the value specified with LT.

$sel:filterVariable:DescribeMLModels', describeMLModels_filterVariable - Use one of the following variables to filter a list of MLModel:

CreatedAt - Sets the search criteria to MLModel creation date.
Status - Sets the search criteria to MLModel status.
Name - Sets the search criteria to the contents of MLModel ____ Name.
IAMUser - Sets the search criteria to the user account that invoked the MLModel creation.
TrainingDataSourceId - Sets the search criteria to the DataSource used to train one or more MLModel.
RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time endpoint status.
MLModelType - Sets the search criteria to MLModel type: binary, regression, or multi-class.
Algorithm - Sets the search criteria to the algorithm that the MLModel uses.
TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

$sel:le:DescribeMLModels', describeMLModels_le - The less than or equal to operator. The MLModel results will have FilterVariable values that are less than or equal to the value specified with LE.

data DescribeMLModelsResponse Source #

Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel.

See: newDescribeMLModelsResponse smart constructor.

Constructors

DescribeMLModelsResponse' (Maybe [MLModel]) (Maybe Text) Int

Instances

Instances details

Eq DescribeMLModelsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods (==) :: DescribeMLModelsResponse -> DescribeMLModelsResponse -> Bool # (/=) :: DescribeMLModelsResponse -> DescribeMLModelsResponse -> Bool #
Read DescribeMLModelsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods readsPrec :: Int -> ReadS DescribeMLModelsResponse # readList :: ReadS [DescribeMLModelsResponse] # readPrec :: ReadPrec DescribeMLModelsResponse # readListPrec :: ReadPrec [DescribeMLModelsResponse] #
Show DescribeMLModelsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods showsPrec :: Int -> DescribeMLModelsResponse -> ShowS # show :: DescribeMLModelsResponse -> String # showList :: [DescribeMLModelsResponse] -> ShowS #
Generic DescribeMLModelsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Associated Types type Rep DescribeMLModelsResponse :: Type -> Type # Methods from :: DescribeMLModelsResponse -> Rep DescribeMLModelsResponse x # to :: Rep DescribeMLModelsResponse x -> DescribeMLModelsResponse #
NFData DescribeMLModelsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels Methods rnf :: DescribeMLModelsResponse -> () #
type Rep DescribeMLModelsResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeMLModels type Rep DescribeMLModelsResponse = D1 ('MetaData "DescribeMLModelsResponse" "Amazonka.MachineLearning.DescribeMLModels" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeMLModelsResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "results") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [MLModel])) :: (S1 ('MetaSel ('Just "nextToken") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newDescribeMLModelsResponse Source #

Arguments

:: Int	`$sel:httpStatus:DescribeMLModelsResponse'`
-> DescribeMLModelsResponse

Create a value of DescribeMLModelsResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:results:DescribeMLModelsResponse', describeMLModelsResponse_results - A list of MLModel that meet the search criteria.

$sel:nextToken:DescribeMLModels', describeMLModelsResponse_nextToken - The ID of the next page in the paginated results that indicates at least one more page follows.

$sel:httpStatus:DescribeMLModelsResponse', describeMLModelsResponse_httpStatus - The response's http status code.

DescribeDataSources (Paginated)

data DescribeDataSources Source #

See: newDescribeDataSources smart constructor.

Constructors

DescribeDataSources' (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe SortOrder) (Maybe Natural) (Maybe Text) (Maybe DataSourceFilterVariable) (Maybe Text)

Instances

Instances details

Eq DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods (==) :: DescribeDataSources -> DescribeDataSources -> Bool # (/=) :: DescribeDataSources -> DescribeDataSources -> Bool #
Read DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods readsPrec :: Int -> ReadS DescribeDataSources # readList :: ReadS [DescribeDataSources] # readPrec :: ReadPrec DescribeDataSources # readListPrec :: ReadPrec [DescribeDataSources] #
Show DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods showsPrec :: Int -> DescribeDataSources -> ShowS # show :: DescribeDataSources -> String # showList :: [DescribeDataSources] -> ShowS #
Generic DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Associated Types type Rep DescribeDataSources :: Type -> Type # Methods from :: DescribeDataSources -> Rep DescribeDataSources x # to :: Rep DescribeDataSources x -> DescribeDataSources #
NFData DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods rnf :: DescribeDataSources -> () #
Hashable DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods hashWithSalt :: Int -> DescribeDataSources -> Int # hash :: DescribeDataSources -> Int #
ToJSON DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods toJSON :: DescribeDataSources -> Value # toEncoding :: DescribeDataSources -> Encoding # toJSONList :: [DescribeDataSources] -> Value # toEncodingList :: [DescribeDataSources] -> Encoding #
AWSPager DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods page :: DescribeDataSources -> AWSResponse DescribeDataSources -> Maybe DescribeDataSources #
AWSRequest DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Associated Types type AWSResponse DescribeDataSources # Methods request :: DescribeDataSources -> Request DescribeDataSources # response :: MonadResource m => Logger -> Service -> Proxy DescribeDataSources -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse DescribeDataSources))) #
ToHeaders DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods toHeaders :: DescribeDataSources -> [Header] #
ToPath DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods toPath :: DescribeDataSources -> ByteString #
ToQuery DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods toQuery :: DescribeDataSources -> QueryString #
type Rep DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources type Rep DescribeDataSources = D1 ('MetaData "DescribeDataSources" "Amazonka.MachineLearning.DescribeDataSources" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeDataSources'" 'PrefixI 'True) (((S1 ('MetaSel ('Just "eq") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "ge") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "prefix") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "gt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "ne") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))) :: ((S1 ('MetaSel ('Just "nextToken") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "sortOrder") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe SortOrder)) :: S1 ('MetaSel ('Just "limit") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Natural)))) :: (S1 ('MetaSel ('Just "lt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "filterVariable") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe DataSourceFilterVariable)) :: S1 ('MetaSel ('Just "le") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))))
type AWSResponse DescribeDataSources Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources type AWSResponse DescribeDataSources = DescribeDataSourcesResponse

newDescribeDataSources :: DescribeDataSources Source #

Create a value of DescribeDataSources with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:eq:DescribeDataSources', describeDataSources_eq - The equal to operator. The DataSource results will have FilterVariable values that exactly match the value specified with EQ.

$sel:ge:DescribeDataSources', describeDataSources_ge - The greater than or equal to operator. The DataSource results will have FilterVariable values that are greater than or equal to the value specified with GE.

$sel:prefix:DescribeDataSources', describeDataSources_prefix - A string that is found at the beginning of a variable, such as Name or Id.

For example, a DataSource could have the Name 2014-09-09-HolidayGiftMailer. To search for this DataSource, select Name for the FilterVariable and any of the following strings for the Prefix:

2014-09
2014-09-09
2014-09-09-Holiday

$sel:gt:DescribeDataSources', describeDataSources_gt - The greater than operator. The DataSource results will have FilterVariable values that are greater than the value specified with GT.

$sel:ne:DescribeDataSources', describeDataSources_ne - The not equal to operator. The DataSource results will have FilterVariable values not equal to the value specified with NE.

$sel:nextToken:DescribeDataSources', describeDataSources_nextToken - The ID of the page in the paginated results.

$sel:sortOrder:DescribeDataSources', describeDataSources_sortOrder - A two-value parameter that determines the sequence of the resulting list of DataSource.

asc - Arranges the list in ascending order (A-Z, 0-9).
dsc - Arranges the list in descending order (Z-A, 9-0).

Results are sorted by FilterVariable.

$sel:limit:DescribeDataSources', describeDataSources_limit - The maximum number of DataSource to include in the result.

$sel:lt:DescribeDataSources', describeDataSources_lt - The less than operator. The DataSource results will have FilterVariable values that are less than the value specified with LT.

$sel:filterVariable:DescribeDataSources', describeDataSources_filterVariable - Use one of the following variables to filter a list of DataSource:

CreatedAt - Sets the search criteria to DataSource creation dates.
Status - Sets the search criteria to DataSource statuses.
Name - Sets the search criteria to the contents of DataSource Name.
DataUri - Sets the search criteria to the URI of data files used to create the DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
IAMUser - Sets the search criteria to the user account that invoked the DataSource creation.

$sel:le:DescribeDataSources', describeDataSources_le - The less than or equal to operator. The DataSource results will have FilterVariable values that are less than or equal to the value specified with LE.

data DescribeDataSourcesResponse Source #

Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.

See: newDescribeDataSourcesResponse smart constructor.

Constructors

DescribeDataSourcesResponse' (Maybe [DataSource]) (Maybe Text) Int

Instances

Instances details

Eq DescribeDataSourcesResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods (==) :: DescribeDataSourcesResponse -> DescribeDataSourcesResponse -> Bool # (/=) :: DescribeDataSourcesResponse -> DescribeDataSourcesResponse -> Bool #
Read DescribeDataSourcesResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods readsPrec :: Int -> ReadS DescribeDataSourcesResponse # readList :: ReadS [DescribeDataSourcesResponse] # readPrec :: ReadPrec DescribeDataSourcesResponse # readListPrec :: ReadPrec [DescribeDataSourcesResponse] #
Show DescribeDataSourcesResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods showsPrec :: Int -> DescribeDataSourcesResponse -> ShowS # show :: DescribeDataSourcesResponse -> String # showList :: [DescribeDataSourcesResponse] -> ShowS #
Generic DescribeDataSourcesResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Associated Types type Rep DescribeDataSourcesResponse :: Type -> Type # Methods from :: DescribeDataSourcesResponse -> Rep DescribeDataSourcesResponse x # to :: Rep DescribeDataSourcesResponse x -> DescribeDataSourcesResponse #
NFData DescribeDataSourcesResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources Methods rnf :: DescribeDataSourcesResponse -> () #
type Rep DescribeDataSourcesResponse Source #
Instance details Defined in Amazonka.MachineLearning.DescribeDataSources type Rep DescribeDataSourcesResponse = D1 ('MetaData "DescribeDataSourcesResponse" "Amazonka.MachineLearning.DescribeDataSources" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DescribeDataSourcesResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "results") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [DataSource])) :: (S1 ('MetaSel ('Just "nextToken") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int))))

newDescribeDataSourcesResponse Source #

Arguments

:: Int	`$sel:httpStatus:DescribeDataSourcesResponse'`
-> DescribeDataSourcesResponse

Create a value of DescribeDataSourcesResponse with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:results:DescribeDataSourcesResponse', describeDataSourcesResponse_results - A list of DataSource that meet the search criteria.

$sel:nextToken:DescribeDataSources', describeDataSourcesResponse_nextToken - An ID of the next page in the paginated results that indicates at least one more page follows.

$sel:httpStatus:DescribeDataSourcesResponse', describeDataSourcesResponse_httpStatus - The response's http status code.

Types

Algorithm

newtype Algorithm Source #

The function used to train an MLModel. Training choices supported by Amazon ML include the following:

SGD - Stochastic Gradient Descent.
RandomForest - Random forest of decision trees.

Constructors

Algorithm'
Fields fromAlgorithm :: Text

Bundled Patterns

pattern Algorithm_Sgd :: Algorithm

Instances

Instances details

Eq Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods (==) :: Algorithm -> Algorithm -> Bool # (/=) :: Algorithm -> Algorithm -> Bool #
Ord Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods compare :: Algorithm -> Algorithm -> Ordering # (<) :: Algorithm -> Algorithm -> Bool # (<=) :: Algorithm -> Algorithm -> Bool # (>) :: Algorithm -> Algorithm -> Bool # (>=) :: Algorithm -> Algorithm -> Bool # max :: Algorithm -> Algorithm -> Algorithm # min :: Algorithm -> Algorithm -> Algorithm #
Read Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods readsPrec :: Int -> ReadS Algorithm # readList :: ReadS [Algorithm] # readPrec :: ReadPrec Algorithm # readListPrec :: ReadPrec [Algorithm] #
Show Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods showsPrec :: Int -> Algorithm -> ShowS # show :: Algorithm -> String # showList :: [Algorithm] -> ShowS #
Generic Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Associated Types type Rep Algorithm :: Type -> Type # Methods from :: Algorithm -> Rep Algorithm x # to :: Rep Algorithm x -> Algorithm #
NFData Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods rnf :: Algorithm -> () #
Hashable Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods hashWithSalt :: Int -> Algorithm -> Int # hash :: Algorithm -> Int #
ToJSON Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods toJSON :: Algorithm -> Value # toEncoding :: Algorithm -> Encoding # toJSONList :: [Algorithm] -> Value # toEncodingList :: [Algorithm] -> Encoding #
ToJSONKey Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods toJSONKey :: ToJSONKeyFunction Algorithm # toJSONKeyList :: ToJSONKeyFunction [Algorithm] #
FromJSON Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods parseJSON :: Value -> Parser Algorithm # parseJSONList :: Value -> Parser [Algorithm] #
FromJSONKey Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods fromJSONKey :: FromJSONKeyFunction Algorithm # fromJSONKeyList :: FromJSONKeyFunction [Algorithm] #
ToLog Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods build :: Algorithm -> ByteStringBuilder #
ToHeader Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods toHeader :: HeaderName -> Algorithm -> [Header] #
ToQuery Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods toQuery :: Algorithm -> QueryString #
FromXML Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods parseXML :: [Node] -> Either String Algorithm #
ToXML Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods toXML :: Algorithm -> XML #
ToByteString Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods toBS :: Algorithm -> ByteString #
FromText Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods fromText :: Text -> Either String Algorithm #
ToText Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm Methods toText :: Algorithm -> Text #
type Rep Algorithm Source #
Instance details Defined in Amazonka.MachineLearning.Types.Algorithm type Rep Algorithm = D1 ('MetaData "Algorithm" "Amazonka.MachineLearning.Types.Algorithm" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "Algorithm'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromAlgorithm") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

BatchPredictionFilterVariable

newtype BatchPredictionFilterVariable Source #

A list of the variables to use in searching or filtering BatchPrediction.

CreatedAt - Sets the search criteria to BatchPrediction creation date.
Status - Sets the search criteria to BatchPrediction status.
Name - Sets the search criteria to the contents of BatchPrediction Name.
IAMUser - Sets the search criteria to the user account that invoked the BatchPrediction creation.
MLModelId - Sets the search criteria to the MLModel used in the BatchPrediction.
DataSourceId - Sets the search criteria to the DataSource used in the BatchPrediction.
DataURI - Sets the search criteria to the data file(s) used in the BatchPrediction. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

Constructors

BatchPredictionFilterVariable'
Fields fromBatchPredictionFilterVariable :: Text

Bundled Patterns

pattern BatchPredictionFilterVariable_CreatedAt :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_DataSourceId :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_DataURI :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_IAMUser :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_LastUpdatedAt :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_MLModelId :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_Name :: BatchPredictionFilterVariable
pattern BatchPredictionFilterVariable_Status :: BatchPredictionFilterVariable

Instances

Instances details

Eq BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods (==) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # (/=) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool #
Ord BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods compare :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Ordering # (<) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # (<=) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # (>) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # (>=) :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> Bool # max :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> BatchPredictionFilterVariable # min :: BatchPredictionFilterVariable -> BatchPredictionFilterVariable -> BatchPredictionFilterVariable #
Read BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods readsPrec :: Int -> ReadS BatchPredictionFilterVariable # readList :: ReadS [BatchPredictionFilterVariable] # readPrec :: ReadPrec BatchPredictionFilterVariable # readListPrec :: ReadPrec [BatchPredictionFilterVariable] #
Show BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods showsPrec :: Int -> BatchPredictionFilterVariable -> ShowS # show :: BatchPredictionFilterVariable -> String # showList :: [BatchPredictionFilterVariable] -> ShowS #
Generic BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Associated Types type Rep BatchPredictionFilterVariable :: Type -> Type # Methods from :: BatchPredictionFilterVariable -> Rep BatchPredictionFilterVariable x # to :: Rep BatchPredictionFilterVariable x -> BatchPredictionFilterVariable #
NFData BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods rnf :: BatchPredictionFilterVariable -> () #
Hashable BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods hashWithSalt :: Int -> BatchPredictionFilterVariable -> Int # hash :: BatchPredictionFilterVariable -> Int #
ToJSON BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods toJSON :: BatchPredictionFilterVariable -> Value # toEncoding :: BatchPredictionFilterVariable -> Encoding # toJSONList :: [BatchPredictionFilterVariable] -> Value # toEncodingList :: [BatchPredictionFilterVariable] -> Encoding #
ToJSONKey BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods toJSONKey :: ToJSONKeyFunction BatchPredictionFilterVariable # toJSONKeyList :: ToJSONKeyFunction [BatchPredictionFilterVariable] #
FromJSON BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods parseJSON :: Value -> Parser BatchPredictionFilterVariable # parseJSONList :: Value -> Parser [BatchPredictionFilterVariable] #
FromJSONKey BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods fromJSONKey :: FromJSONKeyFunction BatchPredictionFilterVariable # fromJSONKeyList :: FromJSONKeyFunction [BatchPredictionFilterVariable] #
ToLog BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods build :: BatchPredictionFilterVariable -> ByteStringBuilder #
ToHeader BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods toHeader :: HeaderName -> BatchPredictionFilterVariable -> [Header] #
ToQuery BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods toQuery :: BatchPredictionFilterVariable -> QueryString #
FromXML BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods parseXML :: [Node] -> Either String BatchPredictionFilterVariable #
ToXML BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods toXML :: BatchPredictionFilterVariable -> XML #
ToByteString BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods toBS :: BatchPredictionFilterVariable -> ByteString #
FromText BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods fromText :: Text -> Either String BatchPredictionFilterVariable #
ToText BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable Methods toText :: BatchPredictionFilterVariable -> Text #
type Rep BatchPredictionFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPredictionFilterVariable type Rep BatchPredictionFilterVariable = D1 ('MetaData "BatchPredictionFilterVariable" "Amazonka.MachineLearning.Types.BatchPredictionFilterVariable" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "BatchPredictionFilterVariable'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromBatchPredictionFilterVariable") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

DataSourceFilterVariable

newtype DataSourceFilterVariable Source #

A list of the variables to use in searching or filtering DataSource.

CreatedAt - Sets the search criteria to DataSource creation date.
Status - Sets the search criteria to DataSource status.
Name - Sets the search criteria to the contents of DataSource Name.
DataUri - Sets the search criteria to the URI of data files used to create the DataSource. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.
IAMUser - Sets the search criteria to the user account that invoked the DataSource creation.

Note: The variable names should match the variable names in the DataSource.

Constructors

DataSourceFilterVariable'
Fields fromDataSourceFilterVariable :: Text

Bundled Patterns

pattern DataSourceFilterVariable_CreatedAt :: DataSourceFilterVariable
pattern DataSourceFilterVariable_DataLocationS3 :: DataSourceFilterVariable
pattern DataSourceFilterVariable_IAMUser :: DataSourceFilterVariable
pattern DataSourceFilterVariable_LastUpdatedAt :: DataSourceFilterVariable
pattern DataSourceFilterVariable_Name :: DataSourceFilterVariable
pattern DataSourceFilterVariable_Status :: DataSourceFilterVariable

Instances

Instances details

Eq DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods (==) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # (/=) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool #
Ord DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods compare :: DataSourceFilterVariable -> DataSourceFilterVariable -> Ordering # (<) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # (<=) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # (>) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # (>=) :: DataSourceFilterVariable -> DataSourceFilterVariable -> Bool # max :: DataSourceFilterVariable -> DataSourceFilterVariable -> DataSourceFilterVariable # min :: DataSourceFilterVariable -> DataSourceFilterVariable -> DataSourceFilterVariable #
Read DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods readsPrec :: Int -> ReadS DataSourceFilterVariable # readList :: ReadS [DataSourceFilterVariable] # readPrec :: ReadPrec DataSourceFilterVariable # readListPrec :: ReadPrec [DataSourceFilterVariable] #
Show DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods showsPrec :: Int -> DataSourceFilterVariable -> ShowS # show :: DataSourceFilterVariable -> String # showList :: [DataSourceFilterVariable] -> ShowS #
Generic DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Associated Types type Rep DataSourceFilterVariable :: Type -> Type # Methods from :: DataSourceFilterVariable -> Rep DataSourceFilterVariable x # to :: Rep DataSourceFilterVariable x -> DataSourceFilterVariable #
NFData DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods rnf :: DataSourceFilterVariable -> () #
Hashable DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods hashWithSalt :: Int -> DataSourceFilterVariable -> Int # hash :: DataSourceFilterVariable -> Int #
ToJSON DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods toJSON :: DataSourceFilterVariable -> Value # toEncoding :: DataSourceFilterVariable -> Encoding # toJSONList :: [DataSourceFilterVariable] -> Value # toEncodingList :: [DataSourceFilterVariable] -> Encoding #
ToJSONKey DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods toJSONKey :: ToJSONKeyFunction DataSourceFilterVariable # toJSONKeyList :: ToJSONKeyFunction [DataSourceFilterVariable] #
FromJSON DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods parseJSON :: Value -> Parser DataSourceFilterVariable # parseJSONList :: Value -> Parser [DataSourceFilterVariable] #
FromJSONKey DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods fromJSONKey :: FromJSONKeyFunction DataSourceFilterVariable # fromJSONKeyList :: FromJSONKeyFunction [DataSourceFilterVariable] #
ToLog DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods build :: DataSourceFilterVariable -> ByteStringBuilder #
ToHeader DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods toHeader :: HeaderName -> DataSourceFilterVariable -> [Header] #
ToQuery DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods toQuery :: DataSourceFilterVariable -> QueryString #
FromXML DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods parseXML :: [Node] -> Either String DataSourceFilterVariable #
ToXML DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods toXML :: DataSourceFilterVariable -> XML #
ToByteString DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods toBS :: DataSourceFilterVariable -> ByteString #
FromText DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods fromText :: Text -> Either String DataSourceFilterVariable #
ToText DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable Methods toText :: DataSourceFilterVariable -> Text #
type Rep DataSourceFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSourceFilterVariable type Rep DataSourceFilterVariable = D1 ('MetaData "DataSourceFilterVariable" "Amazonka.MachineLearning.Types.DataSourceFilterVariable" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "DataSourceFilterVariable'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromDataSourceFilterVariable") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

DetailsAttributes

newtype DetailsAttributes Source #

Contains the key values of DetailsMap:

PredictiveModelType - Indicates the type of the MLModel.
Algorithm - Indicates the algorithm that was used for the MLModel.

Constructors

DetailsAttributes'
Fields fromDetailsAttributes :: Text

Bundled Patterns

pattern DetailsAttributes_Algorithm :: DetailsAttributes
pattern DetailsAttributes_PredictiveModelType :: DetailsAttributes

Instances

Instances details

Eq DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods (==) :: DetailsAttributes -> DetailsAttributes -> Bool # (/=) :: DetailsAttributes -> DetailsAttributes -> Bool #
Ord DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods compare :: DetailsAttributes -> DetailsAttributes -> Ordering # (<) :: DetailsAttributes -> DetailsAttributes -> Bool # (<=) :: DetailsAttributes -> DetailsAttributes -> Bool # (>) :: DetailsAttributes -> DetailsAttributes -> Bool # (>=) :: DetailsAttributes -> DetailsAttributes -> Bool # max :: DetailsAttributes -> DetailsAttributes -> DetailsAttributes # min :: DetailsAttributes -> DetailsAttributes -> DetailsAttributes #
Read DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods readsPrec :: Int -> ReadS DetailsAttributes # readList :: ReadS [DetailsAttributes] # readPrec :: ReadPrec DetailsAttributes # readListPrec :: ReadPrec [DetailsAttributes] #
Show DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods showsPrec :: Int -> DetailsAttributes -> ShowS # show :: DetailsAttributes -> String # showList :: [DetailsAttributes] -> ShowS #
Generic DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Associated Types type Rep DetailsAttributes :: Type -> Type # Methods from :: DetailsAttributes -> Rep DetailsAttributes x # to :: Rep DetailsAttributes x -> DetailsAttributes #
NFData DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods rnf :: DetailsAttributes -> () #
Hashable DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods hashWithSalt :: Int -> DetailsAttributes -> Int # hash :: DetailsAttributes -> Int #
ToJSON DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods toJSON :: DetailsAttributes -> Value # toEncoding :: DetailsAttributes -> Encoding # toJSONList :: [DetailsAttributes] -> Value # toEncodingList :: [DetailsAttributes] -> Encoding #
ToJSONKey DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods toJSONKey :: ToJSONKeyFunction DetailsAttributes # toJSONKeyList :: ToJSONKeyFunction [DetailsAttributes] #
FromJSON DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods parseJSON :: Value -> Parser DetailsAttributes # parseJSONList :: Value -> Parser [DetailsAttributes] #
FromJSONKey DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods fromJSONKey :: FromJSONKeyFunction DetailsAttributes # fromJSONKeyList :: FromJSONKeyFunction [DetailsAttributes] #
ToLog DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods build :: DetailsAttributes -> ByteStringBuilder #
ToHeader DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods toHeader :: HeaderName -> DetailsAttributes -> [Header] #
ToQuery DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods toQuery :: DetailsAttributes -> QueryString #
FromXML DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods parseXML :: [Node] -> Either String DetailsAttributes #
ToXML DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods toXML :: DetailsAttributes -> XML #
ToByteString DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods toBS :: DetailsAttributes -> ByteString #
FromText DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods fromText :: Text -> Either String DetailsAttributes #
ToText DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes Methods toText :: DetailsAttributes -> Text #
type Rep DetailsAttributes Source #
Instance details Defined in Amazonka.MachineLearning.Types.DetailsAttributes type Rep DetailsAttributes = D1 ('MetaData "DetailsAttributes" "Amazonka.MachineLearning.Types.DetailsAttributes" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "DetailsAttributes'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromDetailsAttributes") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

EntityStatus

newtype EntityStatus Source #

Object status with the following possible values:

```
PENDING
```
```
INPROGRESS
```
```
FAILED
```
```
COMPLETED
```
```
DELETED
```

Constructors

EntityStatus'
Fields fromEntityStatus :: Text

Bundled Patterns

pattern EntityStatus_COMPLETED :: EntityStatus
pattern EntityStatus_DELETED :: EntityStatus
pattern EntityStatus_FAILED :: EntityStatus
pattern EntityStatus_INPROGRESS :: EntityStatus
pattern EntityStatus_PENDING :: EntityStatus

Instances

Instances details

Eq EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods (==) :: EntityStatus -> EntityStatus -> Bool # (/=) :: EntityStatus -> EntityStatus -> Bool #
Ord EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods compare :: EntityStatus -> EntityStatus -> Ordering # (<) :: EntityStatus -> EntityStatus -> Bool # (<=) :: EntityStatus -> EntityStatus -> Bool # (>) :: EntityStatus -> EntityStatus -> Bool # (>=) :: EntityStatus -> EntityStatus -> Bool # max :: EntityStatus -> EntityStatus -> EntityStatus # min :: EntityStatus -> EntityStatus -> EntityStatus #
Read EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods readsPrec :: Int -> ReadS EntityStatus # readList :: ReadS [EntityStatus] # readPrec :: ReadPrec EntityStatus # readListPrec :: ReadPrec [EntityStatus] #
Show EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods showsPrec :: Int -> EntityStatus -> ShowS # show :: EntityStatus -> String # showList :: [EntityStatus] -> ShowS #
Generic EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Associated Types type Rep EntityStatus :: Type -> Type # Methods from :: EntityStatus -> Rep EntityStatus x # to :: Rep EntityStatus x -> EntityStatus #
NFData EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods rnf :: EntityStatus -> () #
Hashable EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods hashWithSalt :: Int -> EntityStatus -> Int # hash :: EntityStatus -> Int #
ToJSON EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods toJSON :: EntityStatus -> Value # toEncoding :: EntityStatus -> Encoding # toJSONList :: [EntityStatus] -> Value # toEncodingList :: [EntityStatus] -> Encoding #
ToJSONKey EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods toJSONKey :: ToJSONKeyFunction EntityStatus # toJSONKeyList :: ToJSONKeyFunction [EntityStatus] #
FromJSON EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods parseJSON :: Value -> Parser EntityStatus # parseJSONList :: Value -> Parser [EntityStatus] #
FromJSONKey EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods fromJSONKey :: FromJSONKeyFunction EntityStatus # fromJSONKeyList :: FromJSONKeyFunction [EntityStatus] #
ToLog EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods build :: EntityStatus -> ByteStringBuilder #
ToHeader EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods toHeader :: HeaderName -> EntityStatus -> [Header] #
ToQuery EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods toQuery :: EntityStatus -> QueryString #
FromXML EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods parseXML :: [Node] -> Either String EntityStatus #
ToXML EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods toXML :: EntityStatus -> XML #
ToByteString EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods toBS :: EntityStatus -> ByteString #
FromText EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods fromText :: Text -> Either String EntityStatus #
ToText EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus Methods toText :: EntityStatus -> Text #
type Rep EntityStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.EntityStatus type Rep EntityStatus = D1 ('MetaData "EntityStatus" "Amazonka.MachineLearning.Types.EntityStatus" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "EntityStatus'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromEntityStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

EvaluationFilterVariable

newtype EvaluationFilterVariable Source #

A list of the variables to use in searching or filtering Evaluation.

CreatedAt - Sets the search criteria to Evaluation creation date.
Status - Sets the search criteria to Evaluation status.
Name - Sets the search criteria to the contents of Evaluation ____ Name.
IAMUser - Sets the search criteria to the user account that invoked an evaluation.
MLModelId - Sets the search criteria to the Predictor that was evaluated.
DataSourceId - Sets the search criteria to the DataSource used in evaluation.
DataUri - Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.

Constructors

EvaluationFilterVariable'
Fields fromEvaluationFilterVariable :: Text

Bundled Patterns

pattern EvaluationFilterVariable_CreatedAt :: EvaluationFilterVariable
pattern EvaluationFilterVariable_DataSourceId :: EvaluationFilterVariable
pattern EvaluationFilterVariable_DataURI :: EvaluationFilterVariable
pattern EvaluationFilterVariable_IAMUser :: EvaluationFilterVariable
pattern EvaluationFilterVariable_LastUpdatedAt :: EvaluationFilterVariable
pattern EvaluationFilterVariable_MLModelId :: EvaluationFilterVariable
pattern EvaluationFilterVariable_Name :: EvaluationFilterVariable
pattern EvaluationFilterVariable_Status :: EvaluationFilterVariable

Instances

Instances details

Eq EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods (==) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # (/=) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool #
Ord EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods compare :: EvaluationFilterVariable -> EvaluationFilterVariable -> Ordering # (<) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # (<=) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # (>) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # (>=) :: EvaluationFilterVariable -> EvaluationFilterVariable -> Bool # max :: EvaluationFilterVariable -> EvaluationFilterVariable -> EvaluationFilterVariable # min :: EvaluationFilterVariable -> EvaluationFilterVariable -> EvaluationFilterVariable #
Read EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods readsPrec :: Int -> ReadS EvaluationFilterVariable # readList :: ReadS [EvaluationFilterVariable] # readPrec :: ReadPrec EvaluationFilterVariable # readListPrec :: ReadPrec [EvaluationFilterVariable] #
Show EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods showsPrec :: Int -> EvaluationFilterVariable -> ShowS # show :: EvaluationFilterVariable -> String # showList :: [EvaluationFilterVariable] -> ShowS #
Generic EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Associated Types type Rep EvaluationFilterVariable :: Type -> Type # Methods from :: EvaluationFilterVariable -> Rep EvaluationFilterVariable x # to :: Rep EvaluationFilterVariable x -> EvaluationFilterVariable #
NFData EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods rnf :: EvaluationFilterVariable -> () #
Hashable EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods hashWithSalt :: Int -> EvaluationFilterVariable -> Int # hash :: EvaluationFilterVariable -> Int #
ToJSON EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods toJSON :: EvaluationFilterVariable -> Value # toEncoding :: EvaluationFilterVariable -> Encoding # toJSONList :: [EvaluationFilterVariable] -> Value # toEncodingList :: [EvaluationFilterVariable] -> Encoding #
ToJSONKey EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods toJSONKey :: ToJSONKeyFunction EvaluationFilterVariable # toJSONKeyList :: ToJSONKeyFunction [EvaluationFilterVariable] #
FromJSON EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods parseJSON :: Value -> Parser EvaluationFilterVariable # parseJSONList :: Value -> Parser [EvaluationFilterVariable] #
FromJSONKey EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods fromJSONKey :: FromJSONKeyFunction EvaluationFilterVariable # fromJSONKeyList :: FromJSONKeyFunction [EvaluationFilterVariable] #
ToLog EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods build :: EvaluationFilterVariable -> ByteStringBuilder #
ToHeader EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods toHeader :: HeaderName -> EvaluationFilterVariable -> [Header] #
ToQuery EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods toQuery :: EvaluationFilterVariable -> QueryString #
FromXML EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods parseXML :: [Node] -> Either String EvaluationFilterVariable #
ToXML EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods toXML :: EvaluationFilterVariable -> XML #
ToByteString EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods toBS :: EvaluationFilterVariable -> ByteString #
FromText EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods fromText :: Text -> Either String EvaluationFilterVariable #
ToText EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable Methods toText :: EvaluationFilterVariable -> Text #
type Rep EvaluationFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.EvaluationFilterVariable type Rep EvaluationFilterVariable = D1 ('MetaData "EvaluationFilterVariable" "Amazonka.MachineLearning.Types.EvaluationFilterVariable" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "EvaluationFilterVariable'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromEvaluationFilterVariable") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

MLModelFilterVariable

newtype MLModelFilterVariable Source #

Constructors

MLModelFilterVariable'
Fields fromMLModelFilterVariable :: Text

Bundled Patterns

pattern MLModelFilterVariable_Algorithm :: MLModelFilterVariable
pattern MLModelFilterVariable_CreatedAt :: MLModelFilterVariable
pattern MLModelFilterVariable_IAMUser :: MLModelFilterVariable
pattern MLModelFilterVariable_LastUpdatedAt :: MLModelFilterVariable
pattern MLModelFilterVariable_MLModelType :: MLModelFilterVariable
pattern MLModelFilterVariable_Name :: MLModelFilterVariable
pattern MLModelFilterVariable_RealtimeEndpointStatus :: MLModelFilterVariable
pattern MLModelFilterVariable_Status :: MLModelFilterVariable
pattern MLModelFilterVariable_TrainingDataSourceId :: MLModelFilterVariable
pattern MLModelFilterVariable_TrainingDataURI :: MLModelFilterVariable

Instances

Instances details

Eq MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods (==) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # (/=) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool #
Ord MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods compare :: MLModelFilterVariable -> MLModelFilterVariable -> Ordering # (<) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # (<=) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # (>) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # (>=) :: MLModelFilterVariable -> MLModelFilterVariable -> Bool # max :: MLModelFilterVariable -> MLModelFilterVariable -> MLModelFilterVariable # min :: MLModelFilterVariable -> MLModelFilterVariable -> MLModelFilterVariable #
Read MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods readsPrec :: Int -> ReadS MLModelFilterVariable # readList :: ReadS [MLModelFilterVariable] # readPrec :: ReadPrec MLModelFilterVariable # readListPrec :: ReadPrec [MLModelFilterVariable] #
Show MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods showsPrec :: Int -> MLModelFilterVariable -> ShowS # show :: MLModelFilterVariable -> String # showList :: [MLModelFilterVariable] -> ShowS #
Generic MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Associated Types type Rep MLModelFilterVariable :: Type -> Type # Methods from :: MLModelFilterVariable -> Rep MLModelFilterVariable x # to :: Rep MLModelFilterVariable x -> MLModelFilterVariable #
NFData MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods rnf :: MLModelFilterVariable -> () #
Hashable MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods hashWithSalt :: Int -> MLModelFilterVariable -> Int # hash :: MLModelFilterVariable -> Int #
ToJSON MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods toJSON :: MLModelFilterVariable -> Value # toEncoding :: MLModelFilterVariable -> Encoding # toJSONList :: [MLModelFilterVariable] -> Value # toEncodingList :: [MLModelFilterVariable] -> Encoding #
ToJSONKey MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods toJSONKey :: ToJSONKeyFunction MLModelFilterVariable # toJSONKeyList :: ToJSONKeyFunction [MLModelFilterVariable] #
FromJSON MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods parseJSON :: Value -> Parser MLModelFilterVariable # parseJSONList :: Value -> Parser [MLModelFilterVariable] #
FromJSONKey MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods fromJSONKey :: FromJSONKeyFunction MLModelFilterVariable # fromJSONKeyList :: FromJSONKeyFunction [MLModelFilterVariable] #
ToLog MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods build :: MLModelFilterVariable -> ByteStringBuilder #
ToHeader MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods toHeader :: HeaderName -> MLModelFilterVariable -> [Header] #
ToQuery MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods toQuery :: MLModelFilterVariable -> QueryString #
FromXML MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods parseXML :: [Node] -> Either String MLModelFilterVariable #
ToXML MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods toXML :: MLModelFilterVariable -> XML #
ToByteString MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods toBS :: MLModelFilterVariable -> ByteString #
FromText MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods fromText :: Text -> Either String MLModelFilterVariable #
ToText MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable Methods toText :: MLModelFilterVariable -> Text #
type Rep MLModelFilterVariable Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelFilterVariable type Rep MLModelFilterVariable = D1 ('MetaData "MLModelFilterVariable" "Amazonka.MachineLearning.Types.MLModelFilterVariable" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "MLModelFilterVariable'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromMLModelFilterVariable") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

MLModelType

newtype MLModelType Source #

Constructors

MLModelType'
Fields fromMLModelType :: Text

Bundled Patterns

pattern MLModelType_BINARY :: MLModelType
pattern MLModelType_MULTICLASS :: MLModelType
pattern MLModelType_REGRESSION :: MLModelType

Instances

Instances details

Eq MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods (==) :: MLModelType -> MLModelType -> Bool # (/=) :: MLModelType -> MLModelType -> Bool #
Ord MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods compare :: MLModelType -> MLModelType -> Ordering # (<) :: MLModelType -> MLModelType -> Bool # (<=) :: MLModelType -> MLModelType -> Bool # (>) :: MLModelType -> MLModelType -> Bool # (>=) :: MLModelType -> MLModelType -> Bool # max :: MLModelType -> MLModelType -> MLModelType # min :: MLModelType -> MLModelType -> MLModelType #
Read MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods readsPrec :: Int -> ReadS MLModelType # readList :: ReadS [MLModelType] # readPrec :: ReadPrec MLModelType # readListPrec :: ReadPrec [MLModelType] #
Show MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods showsPrec :: Int -> MLModelType -> ShowS # show :: MLModelType -> String # showList :: [MLModelType] -> ShowS #
Generic MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Associated Types type Rep MLModelType :: Type -> Type # Methods from :: MLModelType -> Rep MLModelType x # to :: Rep MLModelType x -> MLModelType #
NFData MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods rnf :: MLModelType -> () #
Hashable MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods hashWithSalt :: Int -> MLModelType -> Int # hash :: MLModelType -> Int #
ToJSON MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods toJSON :: MLModelType -> Value # toEncoding :: MLModelType -> Encoding # toJSONList :: [MLModelType] -> Value # toEncodingList :: [MLModelType] -> Encoding #
ToJSONKey MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods toJSONKey :: ToJSONKeyFunction MLModelType # toJSONKeyList :: ToJSONKeyFunction [MLModelType] #
FromJSON MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods parseJSON :: Value -> Parser MLModelType # parseJSONList :: Value -> Parser [MLModelType] #
FromJSONKey MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods fromJSONKey :: FromJSONKeyFunction MLModelType # fromJSONKeyList :: FromJSONKeyFunction [MLModelType] #
ToLog MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods build :: MLModelType -> ByteStringBuilder #
ToHeader MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods toHeader :: HeaderName -> MLModelType -> [Header] #
ToQuery MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods toQuery :: MLModelType -> QueryString #
FromXML MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods parseXML :: [Node] -> Either String MLModelType #
ToXML MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods toXML :: MLModelType -> XML #
ToByteString MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods toBS :: MLModelType -> ByteString #
FromText MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods fromText :: Text -> Either String MLModelType #
ToText MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType Methods toText :: MLModelType -> Text #
type Rep MLModelType Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModelType type Rep MLModelType = D1 ('MetaData "MLModelType" "Amazonka.MachineLearning.Types.MLModelType" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "MLModelType'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromMLModelType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

RealtimeEndpointStatus

newtype RealtimeEndpointStatus Source #

Constructors

RealtimeEndpointStatus'
Fields fromRealtimeEndpointStatus :: Text

Bundled Patterns

pattern RealtimeEndpointStatus_FAILED :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_NONE :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_READY :: RealtimeEndpointStatus
pattern RealtimeEndpointStatus_UPDATING :: RealtimeEndpointStatus

Instances

Instances details

Eq RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods (==) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # (/=) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool #
Ord RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods compare :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Ordering # (<) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # (<=) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # (>) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # (>=) :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> Bool # max :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> RealtimeEndpointStatus # min :: RealtimeEndpointStatus -> RealtimeEndpointStatus -> RealtimeEndpointStatus #
Read RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods readsPrec :: Int -> ReadS RealtimeEndpointStatus # readList :: ReadS [RealtimeEndpointStatus] # readPrec :: ReadPrec RealtimeEndpointStatus # readListPrec :: ReadPrec [RealtimeEndpointStatus] #
Show RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods showsPrec :: Int -> RealtimeEndpointStatus -> ShowS # show :: RealtimeEndpointStatus -> String # showList :: [RealtimeEndpointStatus] -> ShowS #
Generic RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Associated Types type Rep RealtimeEndpointStatus :: Type -> Type # Methods from :: RealtimeEndpointStatus -> Rep RealtimeEndpointStatus x # to :: Rep RealtimeEndpointStatus x -> RealtimeEndpointStatus #
NFData RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods rnf :: RealtimeEndpointStatus -> () #
Hashable RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods hashWithSalt :: Int -> RealtimeEndpointStatus -> Int # hash :: RealtimeEndpointStatus -> Int #
ToJSON RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods toJSON :: RealtimeEndpointStatus -> Value # toEncoding :: RealtimeEndpointStatus -> Encoding # toJSONList :: [RealtimeEndpointStatus] -> Value # toEncodingList :: [RealtimeEndpointStatus] -> Encoding #
ToJSONKey RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods toJSONKey :: ToJSONKeyFunction RealtimeEndpointStatus # toJSONKeyList :: ToJSONKeyFunction [RealtimeEndpointStatus] #
FromJSON RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods parseJSON :: Value -> Parser RealtimeEndpointStatus # parseJSONList :: Value -> Parser [RealtimeEndpointStatus] #
FromJSONKey RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods fromJSONKey :: FromJSONKeyFunction RealtimeEndpointStatus # fromJSONKeyList :: FromJSONKeyFunction [RealtimeEndpointStatus] #
ToLog RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods build :: RealtimeEndpointStatus -> ByteStringBuilder #
ToHeader RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods toHeader :: HeaderName -> RealtimeEndpointStatus -> [Header] #
ToQuery RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods toQuery :: RealtimeEndpointStatus -> QueryString #
FromXML RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods parseXML :: [Node] -> Either String RealtimeEndpointStatus #
ToXML RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods toXML :: RealtimeEndpointStatus -> XML #
ToByteString RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods toBS :: RealtimeEndpointStatus -> ByteString #
FromText RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods fromText :: Text -> Either String RealtimeEndpointStatus #
ToText RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus Methods toText :: RealtimeEndpointStatus -> Text #
type Rep RealtimeEndpointStatus Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointStatus type Rep RealtimeEndpointStatus = D1 ('MetaData "RealtimeEndpointStatus" "Amazonka.MachineLearning.Types.RealtimeEndpointStatus" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "RealtimeEndpointStatus'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromRealtimeEndpointStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

SortOrder

newtype SortOrder Source #

The sort order specified in a listing condition. Possible values include the following:

asc - Present the information in ascending order (from A-Z).
dsc - Present the information in descending order (from Z-A).

Constructors

SortOrder'
Fields fromSortOrder :: Text

Bundled Patterns

pattern SortOrder_Asc :: SortOrder
pattern SortOrder_Dsc :: SortOrder

Instances

Instances details

Eq SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods (==) :: SortOrder -> SortOrder -> Bool # (/=) :: SortOrder -> SortOrder -> Bool #
Ord SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods compare :: SortOrder -> SortOrder -> Ordering # (<) :: SortOrder -> SortOrder -> Bool # (<=) :: SortOrder -> SortOrder -> Bool # (>) :: SortOrder -> SortOrder -> Bool # (>=) :: SortOrder -> SortOrder -> Bool # max :: SortOrder -> SortOrder -> SortOrder # min :: SortOrder -> SortOrder -> SortOrder #
Read SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods readsPrec :: Int -> ReadS SortOrder # readList :: ReadS [SortOrder] # readPrec :: ReadPrec SortOrder # readListPrec :: ReadPrec [SortOrder] #
Show SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods showsPrec :: Int -> SortOrder -> ShowS # show :: SortOrder -> String # showList :: [SortOrder] -> ShowS #
Generic SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Associated Types type Rep SortOrder :: Type -> Type # Methods from :: SortOrder -> Rep SortOrder x # to :: Rep SortOrder x -> SortOrder #
NFData SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods rnf :: SortOrder -> () #
Hashable SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods hashWithSalt :: Int -> SortOrder -> Int # hash :: SortOrder -> Int #
ToJSON SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods toJSON :: SortOrder -> Value # toEncoding :: SortOrder -> Encoding # toJSONList :: [SortOrder] -> Value # toEncodingList :: [SortOrder] -> Encoding #
ToJSONKey SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods toJSONKey :: ToJSONKeyFunction SortOrder # toJSONKeyList :: ToJSONKeyFunction [SortOrder] #
FromJSON SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods parseJSON :: Value -> Parser SortOrder # parseJSONList :: Value -> Parser [SortOrder] #
FromJSONKey SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods fromJSONKey :: FromJSONKeyFunction SortOrder # fromJSONKeyList :: FromJSONKeyFunction [SortOrder] #
ToLog SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods build :: SortOrder -> ByteStringBuilder #
ToHeader SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods toHeader :: HeaderName -> SortOrder -> [Header] #
ToQuery SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods toQuery :: SortOrder -> QueryString #
FromXML SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods parseXML :: [Node] -> Either String SortOrder #
ToXML SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods toXML :: SortOrder -> XML #
ToByteString SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods toBS :: SortOrder -> ByteString #
FromText SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods fromText :: Text -> Either String SortOrder #
ToText SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder Methods toText :: SortOrder -> Text #
type Rep SortOrder Source #
Instance details Defined in Amazonka.MachineLearning.Types.SortOrder type Rep SortOrder = D1 ('MetaData "SortOrder" "Amazonka.MachineLearning.Types.SortOrder" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "SortOrder'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromSortOrder") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

TaggableResourceType

newtype TaggableResourceType Source #

Constructors

TaggableResourceType'
Fields fromTaggableResourceType :: Text

Bundled Patterns

pattern TaggableResourceType_BatchPrediction :: TaggableResourceType
pattern TaggableResourceType_DataSource :: TaggableResourceType
pattern TaggableResourceType_Evaluation :: TaggableResourceType
pattern TaggableResourceType_MLModel :: TaggableResourceType

Instances

Instances details

Eq TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods (==) :: TaggableResourceType -> TaggableResourceType -> Bool # (/=) :: TaggableResourceType -> TaggableResourceType -> Bool #
Ord TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods compare :: TaggableResourceType -> TaggableResourceType -> Ordering # (<) :: TaggableResourceType -> TaggableResourceType -> Bool # (<=) :: TaggableResourceType -> TaggableResourceType -> Bool # (>) :: TaggableResourceType -> TaggableResourceType -> Bool # (>=) :: TaggableResourceType -> TaggableResourceType -> Bool # max :: TaggableResourceType -> TaggableResourceType -> TaggableResourceType # min :: TaggableResourceType -> TaggableResourceType -> TaggableResourceType #
Read TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods readsPrec :: Int -> ReadS TaggableResourceType # readList :: ReadS [TaggableResourceType] # readPrec :: ReadPrec TaggableResourceType # readListPrec :: ReadPrec [TaggableResourceType] #
Show TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods showsPrec :: Int -> TaggableResourceType -> ShowS # show :: TaggableResourceType -> String # showList :: [TaggableResourceType] -> ShowS #
Generic TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Associated Types type Rep TaggableResourceType :: Type -> Type # Methods from :: TaggableResourceType -> Rep TaggableResourceType x # to :: Rep TaggableResourceType x -> TaggableResourceType #
NFData TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods rnf :: TaggableResourceType -> () #
Hashable TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods hashWithSalt :: Int -> TaggableResourceType -> Int # hash :: TaggableResourceType -> Int #
ToJSON TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods toJSON :: TaggableResourceType -> Value # toEncoding :: TaggableResourceType -> Encoding # toJSONList :: [TaggableResourceType] -> Value # toEncodingList :: [TaggableResourceType] -> Encoding #
ToJSONKey TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods toJSONKey :: ToJSONKeyFunction TaggableResourceType # toJSONKeyList :: ToJSONKeyFunction [TaggableResourceType] #
FromJSON TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods parseJSON :: Value -> Parser TaggableResourceType # parseJSONList :: Value -> Parser [TaggableResourceType] #
FromJSONKey TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods fromJSONKey :: FromJSONKeyFunction TaggableResourceType # fromJSONKeyList :: FromJSONKeyFunction [TaggableResourceType] #
ToLog TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods build :: TaggableResourceType -> ByteStringBuilder #
ToHeader TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods toHeader :: HeaderName -> TaggableResourceType -> [Header] #
ToQuery TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods toQuery :: TaggableResourceType -> QueryString #
FromXML TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods parseXML :: [Node] -> Either String TaggableResourceType #
ToXML TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods toXML :: TaggableResourceType -> XML #
ToByteString TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods toBS :: TaggableResourceType -> ByteString #
FromText TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods fromText :: Text -> Either String TaggableResourceType #
ToText TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType Methods toText :: TaggableResourceType -> Text #
type Rep TaggableResourceType Source #
Instance details Defined in Amazonka.MachineLearning.Types.TaggableResourceType type Rep TaggableResourceType = D1 ('MetaData "TaggableResourceType" "Amazonka.MachineLearning.Types.TaggableResourceType" "libZSservicesZSamazonka-mlZSamazonka-ml" 'True) (C1 ('MetaCons "TaggableResourceType'" 'PrefixI 'True) (S1 ('MetaSel ('Just "fromTaggableResourceType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedLazy) (Rec0 Text)))

BatchPrediction

data BatchPrediction Source #

Represents the output of a GetBatchPrediction operation.

The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.

See: newBatchPrediction smart constructor.

Constructors

BatchPrediction' (Maybe EntityStatus) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Text) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text)

Instances

Instances details

Eq BatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPrediction Methods (==) :: BatchPrediction -> BatchPrediction -> Bool # (/=) :: BatchPrediction -> BatchPrediction -> Bool #
Read BatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPrediction Methods readsPrec :: Int -> ReadS BatchPrediction # readList :: ReadS [BatchPrediction] # readPrec :: ReadPrec BatchPrediction # readListPrec :: ReadPrec [BatchPrediction] #
Show BatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPrediction Methods showsPrec :: Int -> BatchPrediction -> ShowS # show :: BatchPrediction -> String # showList :: [BatchPrediction] -> ShowS #
Generic BatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPrediction Associated Types type Rep BatchPrediction :: Type -> Type # Methods from :: BatchPrediction -> Rep BatchPrediction x # to :: Rep BatchPrediction x -> BatchPrediction #
NFData BatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPrediction Methods rnf :: BatchPrediction -> () #
Hashable BatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPrediction Methods hashWithSalt :: Int -> BatchPrediction -> Int # hash :: BatchPrediction -> Int #
FromJSON BatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPrediction Methods parseJSON :: Value -> Parser BatchPrediction # parseJSONList :: Value -> Parser [BatchPrediction] #
type Rep BatchPrediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.BatchPrediction type Rep BatchPrediction = D1 ('MetaData "BatchPrediction" "Amazonka.MachineLearning.Types.BatchPrediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "BatchPrediction'" 'PrefixI 'True) ((((S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EntityStatus)) :: S1 ('MetaSel ('Just "lastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))) :: (S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "computeTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)))) :: ((S1 ('MetaSel ('Just "inputDataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "batchPredictionDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "totalRecordCount") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer))))) :: (((S1 ('MetaSel ('Just "startedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "batchPredictionId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "finishedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "invalidRecordCount") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)))) :: ((S1 ('MetaSel ('Just "createdByIamUser") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "name") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "outputUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))))

newBatchPrediction :: BatchPrediction Source #

Create a value of BatchPrediction with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:status:BatchPrediction', batchPrediction_status - The status of the BatchPrediction. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.
INPROGRESS - The process is underway.
FAILED - The request to perform a batch prediction did not run to completion. It is not usable.
COMPLETED - The batch prediction process completed successfully.
DELETED - The BatchPrediction is marked as deleted. It is not usable.

$sel:lastUpdatedAt:BatchPrediction', batchPrediction_lastUpdatedAt - The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

$sel:createdAt:BatchPrediction', batchPrediction_createdAt - The time that the BatchPrediction was created. The time is expressed in epoch time.

$sel:computeTime:BatchPrediction', batchPrediction_computeTime - Undocumented member.

$sel:inputDataLocationS3:BatchPrediction', batchPrediction_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

$sel:mLModelId:BatchPrediction', batchPrediction_mLModelId - The ID of the MLModel that generated predictions for the BatchPrediction request.

$sel:batchPredictionDataSourceId:BatchPrediction', batchPrediction_batchPredictionDataSourceId - The ID of the DataSource that points to the group of observations to predict.

$sel:totalRecordCount:BatchPrediction', batchPrediction_totalRecordCount - Undocumented member.

$sel:startedAt:BatchPrediction', batchPrediction_startedAt - Undocumented member.

$sel:batchPredictionId:BatchPrediction', batchPrediction_batchPredictionId - The ID assigned to the BatchPrediction at creation. This value should be identical to the value of the BatchPredictionID in the request.

$sel:finishedAt:BatchPrediction', batchPrediction_finishedAt - Undocumented member.

$sel:invalidRecordCount:BatchPrediction', batchPrediction_invalidRecordCount - Undocumented member.

$sel:createdByIamUser:BatchPrediction', batchPrediction_createdByIamUser - The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

$sel:name:BatchPrediction', batchPrediction_name - A user-supplied name or description of the BatchPrediction.

$sel:message:BatchPrediction', batchPrediction_message - A description of the most recent details about processing the batch prediction request.

$sel:outputUri:BatchPrediction', batchPrediction_outputUri - The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the outputURI field: ':', '//', '/./', '/../'.

DataSource

data DataSource Source #

Represents the output of the GetDataSource operation.

The content consists of the detailed metadata and data file information and the current status of the DataSource.

See: newDataSource smart constructor.

Constructors

DataSource' (Maybe EntityStatus) (Maybe Integer) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe RDSMetadata) (Maybe Integer) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Bool) (Maybe Text) (Maybe RedshiftMetadata) (Maybe Text) (Maybe Text)

Instances

Instances details

Eq DataSource Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSource Methods (==) :: DataSource -> DataSource -> Bool # (/=) :: DataSource -> DataSource -> Bool #
Read DataSource Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSource Methods readsPrec :: Int -> ReadS DataSource # readList :: ReadS [DataSource] # readPrec :: ReadPrec DataSource # readListPrec :: ReadPrec [DataSource] #
Show DataSource Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSource Methods showsPrec :: Int -> DataSource -> ShowS # show :: DataSource -> String # showList :: [DataSource] -> ShowS #
Generic DataSource Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSource Associated Types type Rep DataSource :: Type -> Type # Methods from :: DataSource -> Rep DataSource x # to :: Rep DataSource x -> DataSource #
NFData DataSource Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSource Methods rnf :: DataSource -> () #
Hashable DataSource Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSource Methods hashWithSalt :: Int -> DataSource -> Int # hash :: DataSource -> Int #
FromJSON DataSource Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSource Methods parseJSON :: Value -> Parser DataSource # parseJSONList :: Value -> Parser [DataSource] #
type Rep DataSource Source #
Instance details Defined in Amazonka.MachineLearning.Types.DataSource type Rep DataSource = D1 ('MetaData "DataSource" "Amazonka.MachineLearning.Types.DataSource" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "DataSource'" 'PrefixI 'True) ((((S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EntityStatus)) :: S1 ('MetaSel ('Just "numberOfFiles") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer))) :: (S1 ('MetaSel ('Just "lastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)))) :: ((S1 ('MetaSel ('Just "computeTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: S1 ('MetaSel ('Just "dataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "rDSMetadata") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RDSMetadata)) :: (S1 ('MetaSel ('Just "dataSizeInBytes") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)) :: S1 ('MetaSel ('Just "startedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)))))) :: (((S1 ('MetaSel ('Just "finishedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "createdByIamUser") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "name") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "dataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :: ((S1 ('MetaSel ('Just "computeStatistics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :: S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "redshiftMetadata") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RedshiftMetadata)) :: (S1 ('MetaSel ('Just "dataRearrangement") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "roleARN") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))))))

newDataSource :: DataSource Source #

Create a value of DataSource with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:status:DataSource', dataSource_status - The current status of the DataSource. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create a DataSource.
INPROGRESS - The creation process is underway.
FAILED - The request to create a DataSource did not run to completion. It is not usable.
COMPLETED - The creation process completed successfully.
DELETED - The DataSource is marked as deleted. It is not usable.

$sel:numberOfFiles:DataSource', dataSource_numberOfFiles - The number of data files referenced by the DataSource.

$sel:lastUpdatedAt:DataSource', dataSource_lastUpdatedAt - The time of the most recent edit to the BatchPrediction. The time is expressed in epoch time.

$sel:createdAt:DataSource', dataSource_createdAt - The time that the DataSource was created. The time is expressed in epoch time.

$sel:computeTime:DataSource', dataSource_computeTime - Undocumented member.

$sel:dataSourceId:DataSource', dataSource_dataSourceId - The ID that is assigned to the DataSource during creation.

$sel:rDSMetadata:DataSource', dataSource_rDSMetadata - Undocumented member.

$sel:dataSizeInBytes:DataSource', dataSource_dataSizeInBytes - The total number of observations contained in the data files that the DataSource references.

$sel:startedAt:DataSource', dataSource_startedAt - Undocumented member.

$sel:finishedAt:DataSource', dataSource_finishedAt - Undocumented member.

$sel:createdByIamUser:DataSource', dataSource_createdByIamUser - The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

$sel:name:DataSource', dataSource_name - A user-supplied name or description of the DataSource.

$sel:dataLocationS3:DataSource', dataSource_dataLocationS3 - The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource.

$sel:computeStatistics:DataSource', dataSource_computeStatistics - The parameter is true if statistics need to be generated from the observation data.

$sel:message:DataSource', dataSource_message - A description of the most recent details about creating the DataSource.

$sel:redshiftMetadata:DataSource', dataSource_redshiftMetadata - Undocumented member.

$sel:dataRearrangement:DataSource', dataSource_dataRearrangement - A JSON string that represents the splitting and rearrangement requirement used when this DataSource was created.

$sel:roleARN:DataSource', dataSource_roleARN - Undocumented member.

Evaluation

data Evaluation Source #

Represents the output of GetEvaluation operation.

The content consists of the detailed metadata and data file information and the current status of the Evaluation.

See: newEvaluation smart constructor.

Constructors

Evaluation' (Maybe EntityStatus) (Maybe PerformanceMetrics) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe POSIX) (Maybe POSIX) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text) (Maybe Text)

Instances

Instances details

Eq Evaluation Source #
Instance details Defined in Amazonka.MachineLearning.Types.Evaluation Methods (==) :: Evaluation -> Evaluation -> Bool # (/=) :: Evaluation -> Evaluation -> Bool #
Read Evaluation Source #
Instance details Defined in Amazonka.MachineLearning.Types.Evaluation Methods readsPrec :: Int -> ReadS Evaluation # readList :: ReadS [Evaluation] # readPrec :: ReadPrec Evaluation # readListPrec :: ReadPrec [Evaluation] #
Show Evaluation Source #
Instance details Defined in Amazonka.MachineLearning.Types.Evaluation Methods showsPrec :: Int -> Evaluation -> ShowS # show :: Evaluation -> String # showList :: [Evaluation] -> ShowS #
Generic Evaluation Source #
Instance details Defined in Amazonka.MachineLearning.Types.Evaluation Associated Types type Rep Evaluation :: Type -> Type # Methods from :: Evaluation -> Rep Evaluation x # to :: Rep Evaluation x -> Evaluation #
NFData Evaluation Source #
Instance details Defined in Amazonka.MachineLearning.Types.Evaluation Methods rnf :: Evaluation -> () #
Hashable Evaluation Source #
Instance details Defined in Amazonka.MachineLearning.Types.Evaluation Methods hashWithSalt :: Int -> Evaluation -> Int # hash :: Evaluation -> Int #
FromJSON Evaluation Source #
Instance details Defined in Amazonka.MachineLearning.Types.Evaluation Methods parseJSON :: Value -> Parser Evaluation # parseJSONList :: Value -> Parser [Evaluation] #
type Rep Evaluation Source #
Instance details Defined in Amazonka.MachineLearning.Types.Evaluation type Rep Evaluation = D1 ('MetaData "Evaluation" "Amazonka.MachineLearning.Types.Evaluation" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "Evaluation'" 'PrefixI 'True) (((S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EntityStatus)) :: (S1 ('MetaSel ('Just "performanceMetrics") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe PerformanceMetrics)) :: S1 ('MetaSel ('Just "lastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)))) :: ((S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "computeTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer))) :: (S1 ('MetaSel ('Just "inputDataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))) :: ((S1 ('MetaSel ('Just "startedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: (S1 ('MetaSel ('Just "finishedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "createdByIamUser") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :: ((S1 ('MetaSel ('Just "name") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "evaluationId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "evaluationDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))))

newEvaluation :: Evaluation Source #

Create a value of Evaluation with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:status:Evaluation', evaluation_status - The status of the evaluation. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel.
INPROGRESS - The evaluation is underway.
FAILED - The request to evaluate an MLModel did not run to completion. It is not usable.
COMPLETED - The evaluation process completed successfully.
DELETED - The Evaluation is marked as deleted. It is not usable.

$sel:performanceMetrics:Evaluation', evaluation_performanceMetrics - Measurements of how well the MLModel performed, using observations referenced by the DataSource. One of the following metrics is returned, based on the type of the MLModel:

BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

$sel:lastUpdatedAt:Evaluation', evaluation_lastUpdatedAt - The time of the most recent edit to the Evaluation. The time is expressed in epoch time.

$sel:createdAt:Evaluation', evaluation_createdAt - The time that the Evaluation was created. The time is expressed in epoch time.

$sel:computeTime:Evaluation', evaluation_computeTime - Undocumented member.

$sel:inputDataLocationS3:Evaluation', evaluation_inputDataLocationS3 - The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.

$sel:mLModelId:Evaluation', evaluation_mLModelId - The ID of the MLModel that is the focus of the evaluation.

$sel:startedAt:Evaluation', evaluation_startedAt - Undocumented member.

$sel:finishedAt:Evaluation', evaluation_finishedAt - Undocumented member.

$sel:createdByIamUser:Evaluation', evaluation_createdByIamUser - The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

$sel:name:Evaluation', evaluation_name - A user-supplied name or description of the Evaluation.

$sel:evaluationId:Evaluation', evaluation_evaluationId - The ID that is assigned to the Evaluation at creation.

$sel:message:Evaluation', evaluation_message - A description of the most recent details about evaluating the MLModel.

$sel:evaluationDataSourceId:Evaluation', evaluation_evaluationDataSourceId - The ID of the DataSource that is used to evaluate the MLModel.

MLModel

data MLModel Source #

Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

See: newMLModel smart constructor.

Constructors

MLModel' (Maybe EntityStatus) (Maybe POSIX) (Maybe (HashMap Text Text)) (Maybe POSIX) (Maybe POSIX) (Maybe Integer) (Maybe Text) (Maybe Text) (Maybe Integer) (Maybe POSIX) (Maybe Double) (Maybe POSIX) (Maybe Algorithm) (Maybe Text) (Maybe Text) (Maybe RealtimeEndpointInfo) (Maybe Text) (Maybe Text) (Maybe MLModelType)

Instances

Instances details

Eq MLModel Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModel Methods (==) :: MLModel -> MLModel -> Bool # (/=) :: MLModel -> MLModel -> Bool #
Read MLModel Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModel Methods readsPrec :: Int -> ReadS MLModel # readList :: ReadS [MLModel] # readPrec :: ReadPrec MLModel # readListPrec :: ReadPrec [MLModel] #
Show MLModel Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModel Methods showsPrec :: Int -> MLModel -> ShowS # show :: MLModel -> String # showList :: [MLModel] -> ShowS #
Generic MLModel Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModel Associated Types type Rep MLModel :: Type -> Type # Methods from :: MLModel -> Rep MLModel x # to :: Rep MLModel x -> MLModel #
NFData MLModel Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModel Methods rnf :: MLModel -> () #
Hashable MLModel Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModel Methods hashWithSalt :: Int -> MLModel -> Int # hash :: MLModel -> Int #
FromJSON MLModel Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModel Methods parseJSON :: Value -> Parser MLModel # parseJSONList :: Value -> Parser [MLModel] #
type Rep MLModel Source #
Instance details Defined in Amazonka.MachineLearning.Types.MLModel type Rep MLModel = D1 ('MetaData "MLModel" "Amazonka.MachineLearning.Types.MLModel" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "MLModel'" 'PrefixI 'True) ((((S1 ('MetaSel ('Just "status") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe EntityStatus)) :: S1 ('MetaSel ('Just "lastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX))) :: (S1 ('MetaSel ('Just "trainingParameters") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text))) :: S1 ('MetaSel ('Just "scoreThresholdLastUpdatedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)))) :: ((S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "computeTime") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer))) :: (S1 ('MetaSel ('Just "inputDataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "mLModelId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "sizeInBytes") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Integer)))))) :: (((S1 ('MetaSel ('Just "startedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "scoreThreshold") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double))) :: (S1 ('MetaSel ('Just "finishedAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: (S1 ('MetaSel ('Just "algorithm") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Algorithm)) :: S1 ('MetaSel ('Just "createdByIamUser") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))) :: ((S1 ('MetaSel ('Just "name") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "endpointInfo") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RealtimeEndpointInfo))) :: (S1 ('MetaSel ('Just "trainingDataSourceId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "message") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "mLModelType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe MLModelType))))))))

newMLModel :: MLModel Source #

Create a value of MLModel with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:status:MLModel', mLModel_status - The current status of an MLModel. This element can have one of the following values:

PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
INPROGRESS - The creation process is underway.
FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.
COMPLETED - The creation process completed successfully.
DELETED - The MLModel is marked as deleted. It isn't usable.

$sel:lastUpdatedAt:MLModel', mLModel_lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.

$sel:trainingParameters:MLModel', mLModel_trainingParameters - A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:

sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none.
sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter tends to drive coefficients to zero, resulting in sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount - The coefficient regularization L2 norm, which controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

$sel:scoreThresholdLastUpdatedAt:MLModel', mLModel_scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.

$sel:createdAt:MLModel', mLModel_createdAt - The time that the MLModel was created. The time is expressed in epoch time.

$sel:computeTime:MLModel', mLModel_computeTime - Undocumented member.

$sel:inputDataLocationS3:MLModel', mLModel_inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).

$sel:mLModelId:MLModel', mLModel_mLModelId - The ID assigned to the MLModel at creation.

$sel:sizeInBytes:MLModel', mLModel_sizeInBytes - Undocumented member.

$sel:startedAt:MLModel', mLModel_startedAt - Undocumented member.

$sel:scoreThreshold:MLModel', mLModel_scoreThreshold - Undocumented member.

$sel:finishedAt:MLModel', mLModel_finishedAt - Undocumented member.

$sel:algorithm:MLModel', mLModel_algorithm - The algorithm used to train the MLModel. The following algorithm is supported:

SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.

$sel:createdByIamUser:MLModel', mLModel_createdByIamUser - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.

$sel:name:MLModel', mLModel_name - A user-supplied name or description of the MLModel.

$sel:endpointInfo:MLModel', mLModel_endpointInfo - The current endpoint of the MLModel.

$sel:trainingDataSourceId:MLModel', mLModel_trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.

$sel:message:MLModel', mLModel_message - A description of the most recent details about accessing the MLModel.

$sel:mLModelType:MLModel', mLModel_mLModelType - Identifies the MLModel category. The following are the available types:

REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"
BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".

PerformanceMetrics

data PerformanceMetrics Source #

Measurements of how well the MLModel performed on known observations. One of the following metrics is returned, based on the type of the MLModel:

BinaryAUC: The binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: The regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: The multiclass MLModel uses the F1 score technique to measure performance.

For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.

See: newPerformanceMetrics smart constructor.

Constructors

PerformanceMetrics' (Maybe (HashMap Text Text))

Instances

Instances details

Eq PerformanceMetrics Source #
Instance details Defined in Amazonka.MachineLearning.Types.PerformanceMetrics Methods (==) :: PerformanceMetrics -> PerformanceMetrics -> Bool # (/=) :: PerformanceMetrics -> PerformanceMetrics -> Bool #
Read PerformanceMetrics Source #
Instance details Defined in Amazonka.MachineLearning.Types.PerformanceMetrics Methods readsPrec :: Int -> ReadS PerformanceMetrics # readList :: ReadS [PerformanceMetrics] # readPrec :: ReadPrec PerformanceMetrics # readListPrec :: ReadPrec [PerformanceMetrics] #
Show PerformanceMetrics Source #
Instance details Defined in Amazonka.MachineLearning.Types.PerformanceMetrics Methods showsPrec :: Int -> PerformanceMetrics -> ShowS # show :: PerformanceMetrics -> String # showList :: [PerformanceMetrics] -> ShowS #
Generic PerformanceMetrics Source #
Instance details Defined in Amazonka.MachineLearning.Types.PerformanceMetrics Associated Types type Rep PerformanceMetrics :: Type -> Type # Methods from :: PerformanceMetrics -> Rep PerformanceMetrics x # to :: Rep PerformanceMetrics x -> PerformanceMetrics #
NFData PerformanceMetrics Source #
Instance details Defined in Amazonka.MachineLearning.Types.PerformanceMetrics Methods rnf :: PerformanceMetrics -> () #
Hashable PerformanceMetrics Source #
Instance details Defined in Amazonka.MachineLearning.Types.PerformanceMetrics Methods hashWithSalt :: Int -> PerformanceMetrics -> Int # hash :: PerformanceMetrics -> Int #
FromJSON PerformanceMetrics Source #
Instance details Defined in Amazonka.MachineLearning.Types.PerformanceMetrics Methods parseJSON :: Value -> Parser PerformanceMetrics # parseJSONList :: Value -> Parser [PerformanceMetrics] #
type Rep PerformanceMetrics Source #
Instance details Defined in Amazonka.MachineLearning.Types.PerformanceMetrics type Rep PerformanceMetrics = D1 ('MetaData "PerformanceMetrics" "Amazonka.MachineLearning.Types.PerformanceMetrics" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "PerformanceMetrics'" 'PrefixI 'True) (S1 ('MetaSel ('Just "properties") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Text)))))

newPerformanceMetrics :: PerformanceMetrics Source #

Create a value of PerformanceMetrics with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:properties:PerformanceMetrics', performanceMetrics_properties - Undocumented member.

Prediction

data Prediction Source #

The output from a Predict operation:

Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD
PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.
PredictedScores - Contains the raw classification score corresponding to each label.
PredictedValue - Present for a REGRESSION MLModel request.

See: newPrediction smart constructor.

Constructors

Prediction' (Maybe Double) (Maybe Text) (Maybe (HashMap Text Double)) (Maybe (HashMap DetailsAttributes Text))

Instances

Instances details

Eq Prediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.Prediction Methods (==) :: Prediction -> Prediction -> Bool # (/=) :: Prediction -> Prediction -> Bool #
Read Prediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.Prediction Methods readsPrec :: Int -> ReadS Prediction # readList :: ReadS [Prediction] # readPrec :: ReadPrec Prediction # readListPrec :: ReadPrec [Prediction] #
Show Prediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.Prediction Methods showsPrec :: Int -> Prediction -> ShowS # show :: Prediction -> String # showList :: [Prediction] -> ShowS #
Generic Prediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.Prediction Associated Types type Rep Prediction :: Type -> Type # Methods from :: Prediction -> Rep Prediction x # to :: Rep Prediction x -> Prediction #
NFData Prediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.Prediction Methods rnf :: Prediction -> () #
Hashable Prediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.Prediction Methods hashWithSalt :: Int -> Prediction -> Int # hash :: Prediction -> Int #
FromJSON Prediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.Prediction Methods parseJSON :: Value -> Parser Prediction # parseJSONList :: Value -> Parser [Prediction] #
type Rep Prediction Source #
Instance details Defined in Amazonka.MachineLearning.Types.Prediction type Rep Prediction = D1 ('MetaData "Prediction" "Amazonka.MachineLearning.Types.Prediction" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "Prediction'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "predictedValue") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Double)) :: S1 ('MetaSel ('Just "predictedLabel") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "predictedScores") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap Text Double))) :*: S1 ('MetaSel ('Just "details") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe (HashMap DetailsAttributes Text))))))

newPrediction :: Prediction Source #

Create a value of Prediction with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:predictedValue:Prediction', prediction_predictedValue - The prediction value for REGRESSION MLModel.

$sel:predictedLabel:Prediction', prediction_predictedLabel - The prediction label for either a BINARY or MULTICLASS MLModel.

$sel:predictedScores:Prediction', prediction_predictedScores - Undocumented member.

$sel:details:Prediction', prediction_details - Undocumented member.

RDSDataSpec

data RDSDataSpec Source #

The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.

See: newRDSDataSpec smart constructor.

Constructors

RDSDataSpec' (Maybe Text) (Maybe Text) (Maybe Text) RDSDatabase Text RDSDatabaseCredentials Text Text Text Text [Text]

Instances

Instances details

Eq RDSDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDataSpec Methods (==) :: RDSDataSpec -> RDSDataSpec -> Bool # (/=) :: RDSDataSpec -> RDSDataSpec -> Bool #
Read RDSDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDataSpec Methods readsPrec :: Int -> ReadS RDSDataSpec # readList :: ReadS [RDSDataSpec] # readPrec :: ReadPrec RDSDataSpec # readListPrec :: ReadPrec [RDSDataSpec] #
Show RDSDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDataSpec Methods showsPrec :: Int -> RDSDataSpec -> ShowS # show :: RDSDataSpec -> String # showList :: [RDSDataSpec] -> ShowS #
Generic RDSDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDataSpec Associated Types type Rep RDSDataSpec :: Type -> Type # Methods from :: RDSDataSpec -> Rep RDSDataSpec x # to :: Rep RDSDataSpec x -> RDSDataSpec #
NFData RDSDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDataSpec Methods rnf :: RDSDataSpec -> () #
Hashable RDSDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDataSpec Methods hashWithSalt :: Int -> RDSDataSpec -> Int # hash :: RDSDataSpec -> Int #
ToJSON RDSDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDataSpec Methods toJSON :: RDSDataSpec -> Value # toEncoding :: RDSDataSpec -> Encoding # toJSONList :: [RDSDataSpec] -> Value # toEncodingList :: [RDSDataSpec] -> Encoding #
type Rep RDSDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDataSpec type Rep RDSDataSpec = D1 ('MetaData "RDSDataSpec" "Amazonka.MachineLearning.Types.RDSDataSpec" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RDSDataSpec'" 'PrefixI 'True) (((S1 ('MetaSel ('Just "dataSchemaUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "dataSchema") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "dataRearrangement") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "databaseInformation") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 RDSDatabase) :: S1 ('MetaSel ('Just "selectSqlQuery") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))) :: ((S1 ('MetaSel ('Just "databaseCredentials") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 RDSDatabaseCredentials) :: (S1 ('MetaSel ('Just "s3StagingLocation") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: S1 ('MetaSel ('Just "resourceRole") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text))) :: (S1 ('MetaSel ('Just "serviceRole") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: (S1 ('MetaSel ('Just "subnetId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :: S1 ('MetaSel ('Just "securityGroupIds") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 [Text]))))))

newRDSDataSpec Source #

Arguments

:: RDSDatabase	`$sel:databaseInformation:RDSDataSpec'`
-> Text	`$sel:selectSqlQuery:RDSDataSpec'`
-> RDSDatabaseCredentials	`$sel:databaseCredentials:RDSDataSpec'`
-> Text	`$sel:s3StagingLocation:RDSDataSpec'`
-> Text	`$sel:resourceRole:RDSDataSpec'`
-> Text	`$sel:serviceRole:RDSDataSpec'`
-> Text	`$sel:subnetId:RDSDataSpec'`
-> RDSDataSpec

Create a value of RDSDataSpec with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSchemaUri:RDSDataSpec', rDSDataSpec_dataSchemaUri - The Amazon S3 location of the DataSchema.

$sel:dataSchema:RDSDataSpec', rDSDataSpec_dataSchema - A JSON string that represents the schema for an Amazon RDS DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

A DataSchema is not required if you specify a DataSchemaUri

Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

$sel:dataRearrangement:RDSDataSpec', rDSDataSpec_dataRearrangement - A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

percentBegin
Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
percentEnd
Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
complement
The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.
For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
strategy
To change how Amazon ML splits the data for a datasource, use the strategy parameter.
The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.
The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:
Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.
The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:
Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

$sel:databaseInformation:RDSDataSpec', rDSDataSpec_databaseInformation - Describes the DatabaseName and InstanceIdentifier of an Amazon RDS database.

$sel:selectSqlQuery:RDSDataSpec', rDSDataSpec_selectSqlQuery - The query that is used to retrieve the observation data for the DataSource.

$sel:databaseCredentials:RDSDataSpec', rDSDataSpec_databaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.

$sel:s3StagingLocation:RDSDataSpec', rDSDataSpec_s3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.

$sel:resourceRole:RDSDataSpec', rDSDataSpec_resourceRole - The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.

$sel:serviceRole:RDSDataSpec', rDSDataSpec_serviceRole - The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

$sel:subnetId:RDSDataSpec', rDSDataSpec_subnetId - The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.

$sel:securityGroupIds:RDSDataSpec', rDSDataSpec_securityGroupIds - The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.

RDSDatabase

data RDSDatabase Source #

The database details of an Amazon RDS database.

See: newRDSDatabase smart constructor.

Constructors

RDSDatabase' Text Text

Instances

Instances details

Eq RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase Methods (==) :: RDSDatabase -> RDSDatabase -> Bool # (/=) :: RDSDatabase -> RDSDatabase -> Bool #
Read RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase Methods readsPrec :: Int -> ReadS RDSDatabase # readList :: ReadS [RDSDatabase] # readPrec :: ReadPrec RDSDatabase # readListPrec :: ReadPrec [RDSDatabase] #
Show RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase Methods showsPrec :: Int -> RDSDatabase -> ShowS # show :: RDSDatabase -> String # showList :: [RDSDatabase] -> ShowS #
Generic RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase Associated Types type Rep RDSDatabase :: Type -> Type # Methods from :: RDSDatabase -> Rep RDSDatabase x # to :: Rep RDSDatabase x -> RDSDatabase #
NFData RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase Methods rnf :: RDSDatabase -> () #
Hashable RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase Methods hashWithSalt :: Int -> RDSDatabase -> Int # hash :: RDSDatabase -> Int #
ToJSON RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase Methods toJSON :: RDSDatabase -> Value # toEncoding :: RDSDatabase -> Encoding # toJSONList :: [RDSDatabase] -> Value # toEncodingList :: [RDSDatabase] -> Encoding #
FromJSON RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase Methods parseJSON :: Value -> Parser RDSDatabase # parseJSONList :: Value -> Parser [RDSDatabase] #
type Rep RDSDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabase type Rep RDSDatabase = D1 ('MetaData "RDSDatabase" "Amazonka.MachineLearning.Types.RDSDatabase" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RDSDatabase'" 'PrefixI 'True) (S1 ('MetaSel ('Just "instanceIdentifier") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "databaseName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))

newRDSDatabase Source #

Arguments

:: Text	`$sel:instanceIdentifier:RDSDatabase'`
-> Text	`$sel:databaseName:RDSDatabase'`
-> RDSDatabase

Create a value of RDSDatabase with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:instanceIdentifier:RDSDatabase', rDSDatabase_instanceIdentifier - The ID of an RDS DB instance.

$sel:databaseName:RDSDatabase', rDSDatabase_databaseName - Undocumented member.

RDSDatabaseCredentials

data RDSDatabaseCredentials Source #

The database credentials to connect to a database on an RDS DB instance.

See: newRDSDatabaseCredentials smart constructor.

Constructors

RDSDatabaseCredentials' Text Text

Instances

Instances details

Eq RDSDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabaseCredentials Methods (==) :: RDSDatabaseCredentials -> RDSDatabaseCredentials -> Bool # (/=) :: RDSDatabaseCredentials -> RDSDatabaseCredentials -> Bool #
Read RDSDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabaseCredentials Methods readsPrec :: Int -> ReadS RDSDatabaseCredentials # readList :: ReadS [RDSDatabaseCredentials] # readPrec :: ReadPrec RDSDatabaseCredentials # readListPrec :: ReadPrec [RDSDatabaseCredentials] #
Show RDSDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabaseCredentials Methods showsPrec :: Int -> RDSDatabaseCredentials -> ShowS # show :: RDSDatabaseCredentials -> String # showList :: [RDSDatabaseCredentials] -> ShowS #
Generic RDSDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabaseCredentials Associated Types type Rep RDSDatabaseCredentials :: Type -> Type # Methods from :: RDSDatabaseCredentials -> Rep RDSDatabaseCredentials x # to :: Rep RDSDatabaseCredentials x -> RDSDatabaseCredentials #
NFData RDSDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabaseCredentials Methods rnf :: RDSDatabaseCredentials -> () #
Hashable RDSDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabaseCredentials Methods hashWithSalt :: Int -> RDSDatabaseCredentials -> Int # hash :: RDSDatabaseCredentials -> Int #
ToJSON RDSDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabaseCredentials Methods toJSON :: RDSDatabaseCredentials -> Value # toEncoding :: RDSDatabaseCredentials -> Encoding # toJSONList :: [RDSDatabaseCredentials] -> Value # toEncodingList :: [RDSDatabaseCredentials] -> Encoding #
type Rep RDSDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSDatabaseCredentials type Rep RDSDatabaseCredentials = D1 ('MetaData "RDSDatabaseCredentials" "Amazonka.MachineLearning.Types.RDSDatabaseCredentials" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RDSDatabaseCredentials'" 'PrefixI 'True) (S1 ('MetaSel ('Just "username") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "password") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))

newRDSDatabaseCredentials Source #

Arguments

:: Text	`$sel:username:RDSDatabaseCredentials'`
-> Text	`$sel:password:RDSDatabaseCredentials'`
-> RDSDatabaseCredentials

Create a value of RDSDatabaseCredentials with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:username:RDSDatabaseCredentials', rDSDatabaseCredentials_username - Undocumented member.

$sel:password:RDSDatabaseCredentials', rDSDatabaseCredentials_password - Undocumented member.

RDSMetadata

data RDSMetadata Source #

The datasource details that are specific to Amazon RDS.

See: newRDSMetadata smart constructor.

Constructors

RDSMetadata' (Maybe Text) (Maybe Text) (Maybe RDSDatabase) (Maybe Text) (Maybe Text) (Maybe Text)

Instances

Instances details

Eq RDSMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSMetadata Methods (==) :: RDSMetadata -> RDSMetadata -> Bool # (/=) :: RDSMetadata -> RDSMetadata -> Bool #
Read RDSMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSMetadata Methods readsPrec :: Int -> ReadS RDSMetadata # readList :: ReadS [RDSMetadata] # readPrec :: ReadPrec RDSMetadata # readListPrec :: ReadPrec [RDSMetadata] #
Show RDSMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSMetadata Methods showsPrec :: Int -> RDSMetadata -> ShowS # show :: RDSMetadata -> String # showList :: [RDSMetadata] -> ShowS #
Generic RDSMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSMetadata Associated Types type Rep RDSMetadata :: Type -> Type # Methods from :: RDSMetadata -> Rep RDSMetadata x # to :: Rep RDSMetadata x -> RDSMetadata #
NFData RDSMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSMetadata Methods rnf :: RDSMetadata -> () #
Hashable RDSMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSMetadata Methods hashWithSalt :: Int -> RDSMetadata -> Int # hash :: RDSMetadata -> Int #
FromJSON RDSMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSMetadata Methods parseJSON :: Value -> Parser RDSMetadata # parseJSONList :: Value -> Parser [RDSMetadata] #
type Rep RDSMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RDSMetadata type Rep RDSMetadata = D1 ('MetaData "RDSMetadata" "Amazonka.MachineLearning.Types.RDSMetadata" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RDSMetadata'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "selectSqlQuery") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "dataPipelineId") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "database") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RDSDatabase)))) :: (S1 ('MetaSel ('Just "databaseUserName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "resourceRole") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "serviceRole") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))))

newRDSMetadata :: RDSMetadata Source #

Create a value of RDSMetadata with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:selectSqlQuery:RDSMetadata', rDSMetadata_selectSqlQuery - The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.

$sel:dataPipelineId:RDSMetadata', rDSMetadata_dataPipelineId - The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.

$sel:database:RDSMetadata', rDSMetadata_database - The database details required to connect to an Amazon RDS.

$sel:databaseUserName:RDSMetadata', rDSMetadata_databaseUserName - Undocumented member.

$sel:resourceRole:RDSMetadata', rDSMetadata_resourceRole - The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

$sel:serviceRole:RDSMetadata', rDSMetadata_serviceRole - The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.

RealtimeEndpointInfo

data RealtimeEndpointInfo Source #

Describes the real-time endpoint information for an MLModel.

See: newRealtimeEndpointInfo smart constructor.

Constructors

RealtimeEndpointInfo' (Maybe POSIX) (Maybe Text) (Maybe RealtimeEndpointStatus) (Maybe Int)

Instances

Instances details

Eq RealtimeEndpointInfo Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointInfo Methods (==) :: RealtimeEndpointInfo -> RealtimeEndpointInfo -> Bool # (/=) :: RealtimeEndpointInfo -> RealtimeEndpointInfo -> Bool #
Read RealtimeEndpointInfo Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointInfo Methods readsPrec :: Int -> ReadS RealtimeEndpointInfo # readList :: ReadS [RealtimeEndpointInfo] # readPrec :: ReadPrec RealtimeEndpointInfo # readListPrec :: ReadPrec [RealtimeEndpointInfo] #
Show RealtimeEndpointInfo Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointInfo Methods showsPrec :: Int -> RealtimeEndpointInfo -> ShowS # show :: RealtimeEndpointInfo -> String # showList :: [RealtimeEndpointInfo] -> ShowS #
Generic RealtimeEndpointInfo Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointInfo Associated Types type Rep RealtimeEndpointInfo :: Type -> Type # Methods from :: RealtimeEndpointInfo -> Rep RealtimeEndpointInfo x # to :: Rep RealtimeEndpointInfo x -> RealtimeEndpointInfo #
NFData RealtimeEndpointInfo Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointInfo Methods rnf :: RealtimeEndpointInfo -> () #
Hashable RealtimeEndpointInfo Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointInfo Methods hashWithSalt :: Int -> RealtimeEndpointInfo -> Int # hash :: RealtimeEndpointInfo -> Int #
FromJSON RealtimeEndpointInfo Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointInfo Methods parseJSON :: Value -> Parser RealtimeEndpointInfo # parseJSONList :: Value -> Parser [RealtimeEndpointInfo] #
type Rep RealtimeEndpointInfo Source #
Instance details Defined in Amazonka.MachineLearning.Types.RealtimeEndpointInfo type Rep RealtimeEndpointInfo = D1 ('MetaData "RealtimeEndpointInfo" "Amazonka.MachineLearning.Types.RealtimeEndpointInfo" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RealtimeEndpointInfo'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "createdAt") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe POSIX)) :: S1 ('MetaSel ('Just "endpointUrl") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "endpointStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RealtimeEndpointStatus)) :*: S1 ('MetaSel ('Just "peakRequestsPerSecond") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Int)))))

newRealtimeEndpointInfo :: RealtimeEndpointInfo Source #

Create a value of RealtimeEndpointInfo with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:createdAt:RealtimeEndpointInfo', realtimeEndpointInfo_createdAt - The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.

$sel:endpointUrl:RealtimeEndpointInfo', realtimeEndpointInfo_endpointUrl - The URI that specifies where to send real-time prediction requests for the MLModel.

Note: The application must wait until the real-time endpoint is ready before using this URI.

$sel:endpointStatus:RealtimeEndpointInfo', realtimeEndpointInfo_endpointStatus - The current status of the real-time endpoint for the MLModel. This element can have one of the following values:

NONE - Endpoint does not exist or was previously deleted.
READY - Endpoint is ready to be used for real-time predictions.
UPDATING - Updating/creating the endpoint.

$sel:peakRequestsPerSecond:RealtimeEndpointInfo', realtimeEndpointInfo_peakRequestsPerSecond - The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.

RedshiftDataSpec

data RedshiftDataSpec Source #

Describes the data specification of an Amazon Redshift DataSource.

See: newRedshiftDataSpec smart constructor.

Constructors

RedshiftDataSpec' (Maybe Text) (Maybe Text) (Maybe Text) RedshiftDatabase Text RedshiftDatabaseCredentials Text

Instances

Instances details

Eq RedshiftDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec Methods (==) :: RedshiftDataSpec -> RedshiftDataSpec -> Bool # (/=) :: RedshiftDataSpec -> RedshiftDataSpec -> Bool #
Read RedshiftDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec Methods readsPrec :: Int -> ReadS RedshiftDataSpec # readList :: ReadS [RedshiftDataSpec] # readPrec :: ReadPrec RedshiftDataSpec # readListPrec :: ReadPrec [RedshiftDataSpec] #
Show RedshiftDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec Methods showsPrec :: Int -> RedshiftDataSpec -> ShowS # show :: RedshiftDataSpec -> String # showList :: [RedshiftDataSpec] -> ShowS #
Generic RedshiftDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec Associated Types type Rep RedshiftDataSpec :: Type -> Type # Methods from :: RedshiftDataSpec -> Rep RedshiftDataSpec x # to :: Rep RedshiftDataSpec x -> RedshiftDataSpec #
NFData RedshiftDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec Methods rnf :: RedshiftDataSpec -> () #
Hashable RedshiftDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec Methods hashWithSalt :: Int -> RedshiftDataSpec -> Int # hash :: RedshiftDataSpec -> Int #
ToJSON RedshiftDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec Methods toJSON :: RedshiftDataSpec -> Value # toEncoding :: RedshiftDataSpec -> Encoding # toJSONList :: [RedshiftDataSpec] -> Value # toEncodingList :: [RedshiftDataSpec] -> Encoding #
type Rep RedshiftDataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDataSpec type Rep RedshiftDataSpec = D1 ('MetaData "RedshiftDataSpec" "Amazonka.MachineLearning.Types.RedshiftDataSpec" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RedshiftDataSpec'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "dataSchemaUri") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "dataSchema") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "dataRearrangement") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))) :: ((S1 ('MetaSel ('Just "databaseInformation") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 RedshiftDatabase) :: S1 ('MetaSel ('Just "selectSqlQuery") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)) :: (S1 ('MetaSel ('Just "databaseCredentials") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 RedshiftDatabaseCredentials) :: S1 ('MetaSel ('Just "s3StagingLocation") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))))

newRedshiftDataSpec Source #

Arguments

:: RedshiftDatabase	`$sel:databaseInformation:RedshiftDataSpec'`
-> Text	`$sel:selectSqlQuery:RedshiftDataSpec'`
-> RedshiftDatabaseCredentials	`$sel:databaseCredentials:RedshiftDataSpec'`
-> Text	`$sel:s3StagingLocation:RedshiftDataSpec'`
-> RedshiftDataSpec

Create a value of RedshiftDataSpec with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSchemaUri:RedshiftDataSpec', redshiftDataSpec_dataSchemaUri - Describes the schema location for an Amazon Redshift DataSource.

$sel:dataSchema:RedshiftDataSpec', redshiftDataSpec_dataSchema - A JSON string that represents the schema for an Amazon Redshift DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

A DataSchema is not required if you specify a DataSchemaUri.

Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

$sel:dataRearrangement:RedshiftDataSpec', redshiftDataSpec_dataRearrangement - A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

percentBegin
Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
percentEnd
Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
complement
The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.
For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
strategy
To change how Amazon ML splits the data for a datasource, use the strategy parameter.
The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.
The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:
Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.
The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:
Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

$sel:databaseInformation:RedshiftDataSpec', redshiftDataSpec_databaseInformation - Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.

$sel:selectSqlQuery:RedshiftDataSpec', redshiftDataSpec_selectSqlQuery - Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.

$sel:databaseCredentials:RedshiftDataSpec', redshiftDataSpec_databaseCredentials - Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.

$sel:s3StagingLocation:RedshiftDataSpec', redshiftDataSpec_s3StagingLocation - Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.

RedshiftDatabase

data RedshiftDatabase Source #

Describes the database details required to connect to an Amazon Redshift database.

See: newRedshiftDatabase smart constructor.

Constructors

RedshiftDatabase' Text Text

Instances

Instances details

Eq RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase Methods (==) :: RedshiftDatabase -> RedshiftDatabase -> Bool # (/=) :: RedshiftDatabase -> RedshiftDatabase -> Bool #
Read RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase Methods readsPrec :: Int -> ReadS RedshiftDatabase # readList :: ReadS [RedshiftDatabase] # readPrec :: ReadPrec RedshiftDatabase # readListPrec :: ReadPrec [RedshiftDatabase] #
Show RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase Methods showsPrec :: Int -> RedshiftDatabase -> ShowS # show :: RedshiftDatabase -> String # showList :: [RedshiftDatabase] -> ShowS #
Generic RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase Associated Types type Rep RedshiftDatabase :: Type -> Type # Methods from :: RedshiftDatabase -> Rep RedshiftDatabase x # to :: Rep RedshiftDatabase x -> RedshiftDatabase #
NFData RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase Methods rnf :: RedshiftDatabase -> () #
Hashable RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase Methods hashWithSalt :: Int -> RedshiftDatabase -> Int # hash :: RedshiftDatabase -> Int #
ToJSON RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase Methods toJSON :: RedshiftDatabase -> Value # toEncoding :: RedshiftDatabase -> Encoding # toJSONList :: [RedshiftDatabase] -> Value # toEncodingList :: [RedshiftDatabase] -> Encoding #
FromJSON RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase Methods parseJSON :: Value -> Parser RedshiftDatabase # parseJSONList :: Value -> Parser [RedshiftDatabase] #
type Rep RedshiftDatabase Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabase type Rep RedshiftDatabase = D1 ('MetaData "RedshiftDatabase" "Amazonka.MachineLearning.Types.RedshiftDatabase" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RedshiftDatabase'" 'PrefixI 'True) (S1 ('MetaSel ('Just "databaseName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "clusterIdentifier") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))

newRedshiftDatabase Source #

Arguments

:: Text	`$sel:databaseName:RedshiftDatabase'`
-> Text	`$sel:clusterIdentifier:RedshiftDatabase'`
-> RedshiftDatabase

Create a value of RedshiftDatabase with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:databaseName:RedshiftDatabase', redshiftDatabase_databaseName - Undocumented member.

$sel:clusterIdentifier:RedshiftDatabase', redshiftDatabase_clusterIdentifier - Undocumented member.

RedshiftDatabaseCredentials

data RedshiftDatabaseCredentials Source #

Describes the database credentials for connecting to a database on an Amazon Redshift cluster.

See: newRedshiftDatabaseCredentials smart constructor.

Constructors

RedshiftDatabaseCredentials' Text Text

Instances

Instances details

Eq RedshiftDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials Methods (==) :: RedshiftDatabaseCredentials -> RedshiftDatabaseCredentials -> Bool # (/=) :: RedshiftDatabaseCredentials -> RedshiftDatabaseCredentials -> Bool #
Read RedshiftDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials Methods readsPrec :: Int -> ReadS RedshiftDatabaseCredentials # readList :: ReadS [RedshiftDatabaseCredentials] # readPrec :: ReadPrec RedshiftDatabaseCredentials # readListPrec :: ReadPrec [RedshiftDatabaseCredentials] #
Show RedshiftDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials Methods showsPrec :: Int -> RedshiftDatabaseCredentials -> ShowS # show :: RedshiftDatabaseCredentials -> String # showList :: [RedshiftDatabaseCredentials] -> ShowS #
Generic RedshiftDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials Associated Types type Rep RedshiftDatabaseCredentials :: Type -> Type # Methods from :: RedshiftDatabaseCredentials -> Rep RedshiftDatabaseCredentials x # to :: Rep RedshiftDatabaseCredentials x -> RedshiftDatabaseCredentials #
NFData RedshiftDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials Methods rnf :: RedshiftDatabaseCredentials -> () #
Hashable RedshiftDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials Methods hashWithSalt :: Int -> RedshiftDatabaseCredentials -> Int # hash :: RedshiftDatabaseCredentials -> Int #
ToJSON RedshiftDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials Methods toJSON :: RedshiftDatabaseCredentials -> Value # toEncoding :: RedshiftDatabaseCredentials -> Encoding # toJSONList :: [RedshiftDatabaseCredentials] -> Value # toEncodingList :: [RedshiftDatabaseCredentials] -> Encoding #
type Rep RedshiftDatabaseCredentials Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials type Rep RedshiftDatabaseCredentials = D1 ('MetaData "RedshiftDatabaseCredentials" "Amazonka.MachineLearning.Types.RedshiftDatabaseCredentials" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RedshiftDatabaseCredentials'" 'PrefixI 'True) (S1 ('MetaSel ('Just "username") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text) :*: S1 ('MetaSel ('Just "password") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))

newRedshiftDatabaseCredentials Source #

Arguments

:: Text	`$sel:username:RedshiftDatabaseCredentials'`
-> Text	`$sel:password:RedshiftDatabaseCredentials'`
-> RedshiftDatabaseCredentials

Create a value of RedshiftDatabaseCredentials with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:username:RedshiftDatabaseCredentials', redshiftDatabaseCredentials_username - Undocumented member.

$sel:password:RedshiftDatabaseCredentials', redshiftDatabaseCredentials_password - Undocumented member.

RedshiftMetadata

data RedshiftMetadata Source #

Describes the DataSource details specific to Amazon Redshift.

See: newRedshiftMetadata smart constructor.

Constructors

RedshiftMetadata' (Maybe Text) (Maybe RedshiftDatabase) (Maybe Text)

Instances

Instances details

Eq RedshiftMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftMetadata Methods (==) :: RedshiftMetadata -> RedshiftMetadata -> Bool # (/=) :: RedshiftMetadata -> RedshiftMetadata -> Bool #
Read RedshiftMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftMetadata Methods readsPrec :: Int -> ReadS RedshiftMetadata # readList :: ReadS [RedshiftMetadata] # readPrec :: ReadPrec RedshiftMetadata # readListPrec :: ReadPrec [RedshiftMetadata] #
Show RedshiftMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftMetadata Methods showsPrec :: Int -> RedshiftMetadata -> ShowS # show :: RedshiftMetadata -> String # showList :: [RedshiftMetadata] -> ShowS #
Generic RedshiftMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftMetadata Associated Types type Rep RedshiftMetadata :: Type -> Type # Methods from :: RedshiftMetadata -> Rep RedshiftMetadata x # to :: Rep RedshiftMetadata x -> RedshiftMetadata #
NFData RedshiftMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftMetadata Methods rnf :: RedshiftMetadata -> () #
Hashable RedshiftMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftMetadata Methods hashWithSalt :: Int -> RedshiftMetadata -> Int # hash :: RedshiftMetadata -> Int #
FromJSON RedshiftMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftMetadata Methods parseJSON :: Value -> Parser RedshiftMetadata # parseJSONList :: Value -> Parser [RedshiftMetadata] #
type Rep RedshiftMetadata Source #
Instance details Defined in Amazonka.MachineLearning.Types.RedshiftMetadata type Rep RedshiftMetadata = D1 ('MetaData "RedshiftMetadata" "Amazonka.MachineLearning.Types.RedshiftMetadata" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "RedshiftMetadata'" 'PrefixI 'True) (S1 ('MetaSel ('Just "selectSqlQuery") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: (S1 ('MetaSel ('Just "redshiftDatabase") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe RedshiftDatabase)) :: S1 ('MetaSel ('Just "databaseUserName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)))))

newRedshiftMetadata :: RedshiftMetadata Source #

Create a value of RedshiftMetadata with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:selectSqlQuery:RedshiftMetadata', redshiftMetadata_selectSqlQuery - The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput.

$sel:redshiftDatabase:RedshiftMetadata', redshiftMetadata_redshiftDatabase - Undocumented member.

$sel:databaseUserName:RedshiftMetadata', redshiftMetadata_databaseUserName - Undocumented member.

S3DataSpec

data S3DataSpec Source #

Describes the data specification of a DataSource.

See: newS3DataSpec smart constructor.

Constructors

S3DataSpec' (Maybe Text) (Maybe Text) (Maybe Text) Text

Instances

Instances details

Eq S3DataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.S3DataSpec Methods (==) :: S3DataSpec -> S3DataSpec -> Bool # (/=) :: S3DataSpec -> S3DataSpec -> Bool #
Read S3DataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.S3DataSpec Methods readsPrec :: Int -> ReadS S3DataSpec # readList :: ReadS [S3DataSpec] # readPrec :: ReadPrec S3DataSpec # readListPrec :: ReadPrec [S3DataSpec] #
Show S3DataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.S3DataSpec Methods showsPrec :: Int -> S3DataSpec -> ShowS # show :: S3DataSpec -> String # showList :: [S3DataSpec] -> ShowS #
Generic S3DataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.S3DataSpec Associated Types type Rep S3DataSpec :: Type -> Type # Methods from :: S3DataSpec -> Rep S3DataSpec x # to :: Rep S3DataSpec x -> S3DataSpec #
NFData S3DataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.S3DataSpec Methods rnf :: S3DataSpec -> () #
Hashable S3DataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.S3DataSpec Methods hashWithSalt :: Int -> S3DataSpec -> Int # hash :: S3DataSpec -> Int #
ToJSON S3DataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.S3DataSpec Methods toJSON :: S3DataSpec -> Value # toEncoding :: S3DataSpec -> Encoding # toJSONList :: [S3DataSpec] -> Value # toEncodingList :: [S3DataSpec] -> Encoding #
type Rep S3DataSpec Source #
Instance details Defined in Amazonka.MachineLearning.Types.S3DataSpec type Rep S3DataSpec = D1 ('MetaData "S3DataSpec" "Amazonka.MachineLearning.Types.S3DataSpec" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "S3DataSpec'" 'PrefixI 'True) ((S1 ('MetaSel ('Just "dataSchema") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :: S1 ('MetaSel ('Just "dataSchemaLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))) :: (S1 ('MetaSel ('Just "dataRearrangement") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "dataLocationS3") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text))))

newS3DataSpec Source #

Arguments

:: Text	`$sel:dataLocationS3:S3DataSpec'`
-> S3DataSpec

Create a value of S3DataSpec with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:dataSchema:S3DataSpec', s3DataSpec_dataSchema - A JSON string that represents the schema for an Amazon S3 DataSource. The DataSchema defines the structure of the observation data in the data file(s) referenced in the DataSource.

You must provide either the DataSchema or the DataSchemaLocationS3.

Define your DataSchema as a series of key-value pairs. attributes and excludedVariableNames have an array of key-value pairs for their value. Use the following format to define your DataSchema.

{ "version": "1.0",

"recordAnnotationFieldName": "F1",

"recordWeightFieldName": "F2",

"targetFieldName": "F3",

"dataFormat": "CSV",

"dataFileContainsHeader": true,

"attributes": [

{ "fieldName": "F1", "fieldType": "TEXT" }, { "fieldName": "F2", "fieldType": "NUMERIC" }, { "fieldName": "F3", "fieldType": "CATEGORICAL" }, { "fieldName": "F4", "fieldType": "NUMERIC" }, { "fieldName": "F5", "fieldType": "CATEGORICAL" }, { "fieldName": "F6", "fieldType": "TEXT" }, { "fieldName": "F7", "fieldType": "WEIGHTED_INT_SEQUENCE" }, { "fieldName": "F8", "fieldType": "WEIGHTED_STRING_SEQUENCE" } ],

"excludedVariableNames": [ "F6" ] }

$sel:dataSchemaLocationS3:S3DataSpec', s3DataSpec_dataSchemaLocationS3 - Describes the schema location in Amazon S3. You must provide either the DataSchema or the DataSchemaLocationS3.

$sel:dataRearrangement:S3DataSpec', s3DataSpec_dataRearrangement - A JSON string that represents the splitting and rearrangement processing to be applied to a DataSource. If the DataRearrangement parameter is not provided, all of the input data is used to create the Datasource.

There are multiple parameters that control what data is used to create a datasource:

percentBegin
Use percentBegin to indicate the beginning of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
percentEnd
Use percentEnd to indicate the end of the range of the data used to create the Datasource. If you do not include percentBegin and percentEnd, Amazon ML includes all of the data when creating the datasource.
complement
The complement parameter instructs Amazon ML to use the data that is not included in the range of percentBegin to percentEnd to create a datasource. The complement parameter is useful if you need to create complementary datasources for training and evaluation. To create a complementary datasource, use the same values for percentBegin and percentEnd, along with the complement parameter.
For example, the following two datasources do not share any data, and can be used to train and evaluate a model. The first datasource has 25 percent of the data, and the second one has 75 percent of the data.
Datasource for evaluation: {"splitting":{"percentBegin":0, "percentEnd":25}}
Datasource for training: {"splitting":{"percentBegin":0, "percentEnd":25, "complement":"true"}}
strategy
To change how Amazon ML splits the data for a datasource, use the strategy parameter.
The default value for the strategy parameter is sequential, meaning that Amazon ML takes all of the data records between the percentBegin and percentEnd parameters for the datasource, in the order that the records appear in the input data.
The following two DataRearrangement lines are examples of sequentially ordered training and evaluation datasources:
Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential"}}
Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"sequential", "complement":"true"}}
To randomly split the input data into the proportions indicated by the percentBegin and percentEnd parameters, set the strategy parameter to random and provide a string that is used as the seed value for the random data splitting (for example, you can use the S3 path to your data as the random seed string). If you choose the random split strategy, Amazon ML assigns each row of data a pseudo-random number between 0 and 100, and then selects the rows that have an assigned number between percentBegin and percentEnd. Pseudo-random numbers are assigned using both the input seed string value and the byte offset as a seed, so changing the data results in a different split. Any existing ordering is preserved. The random splitting strategy ensures that variables in the training and evaluation data are distributed similarly. It is useful in the cases where the input data may have an implicit sort order, which would otherwise result in training and evaluation datasources containing non-similar data records.
The following two DataRearrangement lines are examples of non-sequentially ordered training and evaluation datasources:
Datasource for evaluation: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv"}}
Datasource for training: {"splitting":{"percentBegin":70, "percentEnd":100, "strategy":"random", "randomSeed"="s3://my_s3_path/bucket/file.csv", "complement":"true"}}

$sel:dataLocationS3:S3DataSpec', s3DataSpec_dataLocationS3 - The location of the data file(s) used by a DataSource. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.

Tag

data Tag Source #

A custom key-value pair associated with an ML object, such as an ML model.

See: newTag smart constructor.

Constructors

Tag' (Maybe Text) (Maybe Text)

Instances

Instances details

Eq Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag Methods (==) :: Tag -> Tag -> Bool # (/=) :: Tag -> Tag -> Bool #
Read Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag Methods readsPrec :: Int -> ReadS Tag # readList :: ReadS [Tag] # readPrec :: ReadPrec Tag # readListPrec :: ReadPrec [Tag] #
Show Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag Methods showsPrec :: Int -> Tag -> ShowS # show :: Tag -> String # showList :: [Tag] -> ShowS #
Generic Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag Associated Types type Rep Tag :: Type -> Type # Methods from :: Tag -> Rep Tag x # to :: Rep Tag x -> Tag #
NFData Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag Methods rnf :: Tag -> () #
Hashable Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag Methods hashWithSalt :: Int -> Tag -> Int # hash :: Tag -> Int #
ToJSON Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag Methods toJSON :: Tag -> Value # toEncoding :: Tag -> Encoding # toJSONList :: [Tag] -> Value # toEncodingList :: [Tag] -> Encoding #
FromJSON Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag Methods parseJSON :: Value -> Parser Tag # parseJSONList :: Value -> Parser [Tag] #
type Rep Tag Source #
Instance details Defined in Amazonka.MachineLearning.Types.Tag type Rep Tag = D1 ('MetaData "Tag" "Amazonka.MachineLearning.Types.Tag" "libZSservicesZSamazonka-mlZSamazonka-ml" 'False) (C1 ('MetaCons "Tag'" 'PrefixI 'True) (S1 ('MetaSel ('Just "value") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text)) :*: S1 ('MetaSel ('Just "key") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Text))))

newTag :: Tag Source #

Create a value of Tag with all optional fields omitted.

Use generic-lens or optics to modify other optional fields.

The following record fields are available, with the corresponding lenses provided for backwards compatibility:

$sel:value:Tag', tag_value - An optional string, typically used to describe or define the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.

$sel:key:Tag', tag_key - A unique identifier for the tag. Valid characters include Unicode letters, digits, white space, _, ., /, =, +, -, %, and @.