libZSservicesZSamazonka-sagemakerZSamazonka-sagemaker
Copyright(c) 2013-2021 Brendan Hay
LicenseMozilla Public License, v. 2.0.
MaintainerBrendan Hay <brendan.g.hay+amazonka@gmail.com>
Stabilityauto-generated
Portabilitynon-portable (GHC extensions)
Safe HaskellNone

Amazonka.SageMaker.CreateAutoMLJob

Description

Creates an Autopilot job.

Find the best-performing model after you run an Autopilot job by calling .

For information about how to use Autopilot, see Automate Model Development with Amazon SageMaker Autopilot.

Synopsis

Creating a Request

data CreateAutoMLJob Source #

See: newCreateAutoMLJob smart constructor.

Constructors

CreateAutoMLJob' 

Fields

Instances

Instances details
Eq CreateAutoMLJob Source # 
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Read CreateAutoMLJob Source # 
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Show CreateAutoMLJob Source # 
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Generic CreateAutoMLJob Source # 
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Associated Types

type Rep CreateAutoMLJob :: Type -> Type #

NFData CreateAutoMLJob Source # 
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Methods

rnf :: CreateAutoMLJob -> () #

Hashable CreateAutoMLJob Source # 
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ToJSON CreateAutoMLJob Source # 
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AWSRequest CreateAutoMLJob Source # 
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Associated Types

type AWSResponse CreateAutoMLJob #

ToHeaders CreateAutoMLJob Source # 
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ToPath CreateAutoMLJob Source # 
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ToQuery CreateAutoMLJob Source # 
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type Rep CreateAutoMLJob Source # 
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type AWSResponse CreateAutoMLJob Source # 
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newCreateAutoMLJob Source #

Create a value of CreateAutoMLJob 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:generateCandidateDefinitionsOnly:CreateAutoMLJob', createAutoMLJob_generateCandidateDefinitionsOnly - Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

$sel:problemType:CreateAutoMLJob', createAutoMLJob_problemType - Defines the type of supervised learning available for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

$sel:autoMLJobConfig:CreateAutoMLJob', createAutoMLJob_autoMLJobConfig - Contains CompletionCriteria and SecurityConfig settings for the AutoML job.

$sel:autoMLJobObjective:CreateAutoMLJob', createAutoMLJob_autoMLJobObjective - Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

$sel:modelDeployConfig:CreateAutoMLJob', createAutoMLJob_modelDeployConfig - Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

$sel:tags:CreateAutoMLJob', createAutoMLJob_tags - Each tag consists of a key and an optional value. Tag keys must be unique per resource.

$sel:autoMLJobName:CreateAutoMLJob', createAutoMLJob_autoMLJobName - Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.

$sel:inputDataConfig:CreateAutoMLJob', createAutoMLJob_inputDataConfig - An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV. Minimum of 500 rows.

$sel:outputDataConfig:CreateAutoMLJob', createAutoMLJob_outputDataConfig - Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

$sel:roleArn:CreateAutoMLJob', createAutoMLJob_roleArn - The ARN of the role that is used to access the data.

Request Lenses

createAutoMLJob_generateCandidateDefinitionsOnly :: Lens' CreateAutoMLJob (Maybe Bool) Source #

Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

createAutoMLJob_problemType :: Lens' CreateAutoMLJob (Maybe ProblemType) Source #

Defines the type of supervised learning available for the candidates. Options include: BinaryClassification, MulticlassClassification, and Regression. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

createAutoMLJob_autoMLJobConfig :: Lens' CreateAutoMLJob (Maybe AutoMLJobConfig) Source #

Contains CompletionCriteria and SecurityConfig settings for the AutoML job.

createAutoMLJob_autoMLJobObjective :: Lens' CreateAutoMLJob (Maybe AutoMLJobObjective) Source #

Defines the objective metric used to measure the predictive quality of an AutoML job. You provide an AutoMLJobObjective$MetricName and Autopilot infers whether to minimize or maximize it.

createAutoMLJob_modelDeployConfig :: Lens' CreateAutoMLJob (Maybe ModelDeployConfig) Source #

Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

createAutoMLJob_tags :: Lens' CreateAutoMLJob (Maybe [Tag]) Source #

Each tag consists of a key and an optional value. Tag keys must be unique per resource.

createAutoMLJob_autoMLJobName :: Lens' CreateAutoMLJob Text Source #

Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.

createAutoMLJob_inputDataConfig :: Lens' CreateAutoMLJob (NonEmpty AutoMLChannel) Source #

An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by . Format(s) supported: CSV. Minimum of 500 rows.

createAutoMLJob_outputDataConfig :: Lens' CreateAutoMLJob AutoMLOutputDataConfig Source #

Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

createAutoMLJob_roleArn :: Lens' CreateAutoMLJob Text Source #

The ARN of the role that is used to access the data.

Destructuring the Response

data CreateAutoMLJobResponse Source #

See: newCreateAutoMLJobResponse smart constructor.

Constructors

CreateAutoMLJobResponse' 

Fields

Instances

Instances details
Eq CreateAutoMLJobResponse Source # 
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Read CreateAutoMLJobResponse Source # 
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Show CreateAutoMLJobResponse Source # 
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Generic CreateAutoMLJobResponse Source # 
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Associated Types

type Rep CreateAutoMLJobResponse :: Type -> Type #

NFData CreateAutoMLJobResponse Source # 
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Methods

rnf :: CreateAutoMLJobResponse -> () #

type Rep CreateAutoMLJobResponse Source # 
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type Rep CreateAutoMLJobResponse = D1 ('MetaData "CreateAutoMLJobResponse" "Amazonka.SageMaker.CreateAutoMLJob" "libZSservicesZSamazonka-sagemakerZSamazonka-sagemaker" 'False) (C1 ('MetaCons "CreateAutoMLJobResponse'" 'PrefixI 'True) (S1 ('MetaSel ('Just "httpStatus") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Int) :*: S1 ('MetaSel ('Just "autoMLJobArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)))

newCreateAutoMLJobResponse Source #

Create a value of CreateAutoMLJobResponse 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:httpStatus:CreateAutoMLJobResponse', createAutoMLJobResponse_httpStatus - The response's http status code.

$sel:autoMLJobArn:CreateAutoMLJobResponse', createAutoMLJobResponse_autoMLJobArn - The unique ARN assigned to the AutoML job when it is created.

Response Lenses

createAutoMLJobResponse_autoMLJobArn :: Lens' CreateAutoMLJobResponse Text Source #

The unique ARN assigned to the AutoML job when it is created.