Copyright	(c) 2013-2023 Brendan Hay
License	Mozilla Public License, v. 2.0.
Maintainer	Brendan Hay
Stability	auto-generated
Portability	non-portable (GHC extensions)
Safe Haskell	Safe-Inferred
Language	Haskell2010

Amazonka.SageMaker.CreateAutoMLJob

Contents

Creating a Request
Request Lenses
Destructuring the Response
Response Lenses

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

data CreateAutoMLJob = CreateAutoMLJob' {
- autoMLJobConfig :: Maybe AutoMLJobConfig
- autoMLJobObjective :: Maybe AutoMLJobObjective
- generateCandidateDefinitionsOnly :: Maybe Bool
- modelDeployConfig :: Maybe ModelDeployConfig
- problemType :: Maybe ProblemType
- tags :: Maybe [Tag]
- autoMLJobName :: Text
- inputDataConfig :: NonEmpty AutoMLChannel
- outputDataConfig :: AutoMLOutputDataConfig
- roleArn :: Text
}
newCreateAutoMLJob :: Text -> NonEmpty AutoMLChannel -> AutoMLOutputDataConfig -> Text -> CreateAutoMLJob
createAutoMLJob_autoMLJobConfig :: Lens' CreateAutoMLJob (Maybe AutoMLJobConfig)
createAutoMLJob_autoMLJobObjective :: Lens' CreateAutoMLJob (Maybe AutoMLJobObjective)
createAutoMLJob_generateCandidateDefinitionsOnly :: Lens' CreateAutoMLJob (Maybe Bool)
createAutoMLJob_modelDeployConfig :: Lens' CreateAutoMLJob (Maybe ModelDeployConfig)
createAutoMLJob_problemType :: Lens' CreateAutoMLJob (Maybe ProblemType)
createAutoMLJob_tags :: Lens' CreateAutoMLJob (Maybe [Tag])
createAutoMLJob_autoMLJobName :: Lens' CreateAutoMLJob Text
createAutoMLJob_inputDataConfig :: Lens' CreateAutoMLJob (NonEmpty AutoMLChannel)
createAutoMLJob_outputDataConfig :: Lens' CreateAutoMLJob AutoMLOutputDataConfig
createAutoMLJob_roleArn :: Lens' CreateAutoMLJob Text
data CreateAutoMLJobResponse = CreateAutoMLJobResponse' {
- httpStatus :: Int
- autoMLJobArn :: Text
}
newCreateAutoMLJobResponse :: Int -> Text -> CreateAutoMLJobResponse
createAutoMLJobResponse_httpStatus :: Lens' CreateAutoMLJobResponse Int
createAutoMLJobResponse_autoMLJobArn :: Lens' CreateAutoMLJobResponse Text

Creating a Request

data CreateAutoMLJob Source #

See: newCreateAutoMLJob smart constructor.

Constructors

CreateAutoMLJob'

Fields

autoMLJobConfig :: Maybe AutoMLJobConfig
A collection of settings used to configure an AutoML job.
autoMLJobObjective :: Maybe 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.
generateCandidateDefinitionsOnly :: Maybe Bool
Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
modelDeployConfig :: Maybe ModelDeployConfig
Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
problemType :: Maybe ProblemType
Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.
tags :: Maybe [Tag]
Each tag consists of a key and an optional value. Tag keys must be unique per resource.
autoMLJobName :: Text
Identifies an Autopilot job. The name must be unique to your account and is case-insensitive.
inputDataConfig :: NonEmpty AutoMLChannel
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, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.
outputDataConfig :: AutoMLOutputDataConfig
Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
roleArn :: Text
The ARN of the role that is used to access the data.

Instances

Instances details

ToJSON CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods toJSON :: CreateAutoMLJob -> Value # toEncoding :: CreateAutoMLJob -> Encoding # toJSONList :: [CreateAutoMLJob] -> Value # toEncodingList :: [CreateAutoMLJob] -> Encoding #
ToHeaders CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods toHeaders :: CreateAutoMLJob -> [Header] #
ToPath CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods toPath :: CreateAutoMLJob -> ByteString #
ToQuery CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods toQuery :: CreateAutoMLJob -> QueryString #
AWSRequest CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Associated Types type AWSResponse CreateAutoMLJob # Methods request :: (Service -> Service) -> CreateAutoMLJob -> Request CreateAutoMLJob # response :: MonadResource m => (ByteStringLazy -> IO ByteStringLazy) -> Service -> Proxy CreateAutoMLJob -> ClientResponse ClientBody -> m (Either Error (ClientResponse (AWSResponse CreateAutoMLJob))) #
Generic CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Associated Types type Rep CreateAutoMLJob :: Type -> Type # Methods from :: CreateAutoMLJob -> Rep CreateAutoMLJob x # to :: Rep CreateAutoMLJob x -> CreateAutoMLJob #
Read CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods readsPrec :: Int -> ReadS CreateAutoMLJob # readList :: ReadS [CreateAutoMLJob] # readPrec :: ReadPrec CreateAutoMLJob # readListPrec :: ReadPrec [CreateAutoMLJob] #
Show CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods showsPrec :: Int -> CreateAutoMLJob -> ShowS # show :: CreateAutoMLJob -> String # showList :: [CreateAutoMLJob] -> ShowS #
NFData CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods rnf :: CreateAutoMLJob -> () #
Eq CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods (==) :: CreateAutoMLJob -> CreateAutoMLJob -> Bool # (/=) :: CreateAutoMLJob -> CreateAutoMLJob -> Bool #
Hashable CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods hashWithSalt :: Int -> CreateAutoMLJob -> Int # hash :: CreateAutoMLJob -> Int #
type AWSResponse CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob type AWSResponse CreateAutoMLJob = CreateAutoMLJobResponse
type Rep CreateAutoMLJob Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob type Rep CreateAutoMLJob = D1 ('MetaData "CreateAutoMLJob" "Amazonka.SageMaker.CreateAutoMLJob" "amazonka-sagemaker-2.0-9SyrKZ4KqhsL1qX9u3ILA3" 'False) (C1 ('MetaCons "CreateAutoMLJob'" 'PrefixI 'True) (((S1 ('MetaSel ('Just "autoMLJobConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe AutoMLJobConfig)) :: S1 ('MetaSel ('Just "autoMLJobObjective") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe AutoMLJobObjective))) :: (S1 ('MetaSel ('Just "generateCandidateDefinitionsOnly") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe Bool)) :: (S1 ('MetaSel ('Just "modelDeployConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ModelDeployConfig)) :: S1 ('MetaSel ('Just "problemType") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe ProblemType))))) :: ((S1 ('MetaSel ('Just "tags") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (Maybe [Tag])) :: S1 ('MetaSel ('Just "autoMLJobName") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text)) :: (S1 ('MetaSel ('Just "inputDataConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 (NonEmpty AutoMLChannel)) :: (S1 ('MetaSel ('Just "outputDataConfig") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 AutoMLOutputDataConfig) :*: S1 ('MetaSel ('Just "roleArn") 'NoSourceUnpackedness 'NoSourceStrictness 'DecidedStrict) (Rec0 Text))))))

newCreateAutoMLJob Source #

Arguments

:: Text	`CreateAutoMLJob`
-> NonEmpty AutoMLChannel	`CreateAutoMLJob`
-> AutoMLOutputDataConfig	`CreateAutoMLJob`
-> Text	`CreateAutoMLJob`
-> CreateAutoMLJob

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:autoMLJobConfig:CreateAutoMLJob', createAutoMLJob_autoMLJobConfig - A collection of settings used to configure an AutoML job.

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

CreateAutoMLJob, createAutoMLJob_problemType - Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

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

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

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, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

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.

CreateAutoMLJob, createAutoMLJob_roleArn - The ARN of the role that is used to access the data.

Request Lenses

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

A collection of settings used to configure an 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_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_modelDeployConfig :: Lens' CreateAutoMLJob (Maybe ModelDeployConfig) Source #

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

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

Defines the type of supervised learning available for the candidates. For more information, see Amazon SageMaker Autopilot problem types and algorithm support.

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, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

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 httpStatus :: Int The response's http status code. autoMLJobArn :: Text The unique ARN assigned to the AutoML job when it is created.

Instances

Instances details

Generic CreateAutoMLJobResponse Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Associated Types type Rep CreateAutoMLJobResponse :: Type -> Type # Methods from :: CreateAutoMLJobResponse -> Rep CreateAutoMLJobResponse x # to :: Rep CreateAutoMLJobResponse x -> CreateAutoMLJobResponse #
Read CreateAutoMLJobResponse Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods readsPrec :: Int -> ReadS CreateAutoMLJobResponse # readList :: ReadS [CreateAutoMLJobResponse] # readPrec :: ReadPrec CreateAutoMLJobResponse # readListPrec :: ReadPrec [CreateAutoMLJobResponse] #
Show CreateAutoMLJobResponse Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods showsPrec :: Int -> CreateAutoMLJobResponse -> ShowS # show :: CreateAutoMLJobResponse -> String # showList :: [CreateAutoMLJobResponse] -> ShowS #
NFData CreateAutoMLJobResponse Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods rnf :: CreateAutoMLJobResponse -> () #
Eq CreateAutoMLJobResponse Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob Methods (==) :: CreateAutoMLJobResponse -> CreateAutoMLJobResponse -> Bool # (/=) :: CreateAutoMLJobResponse -> CreateAutoMLJobResponse -> Bool #
type Rep CreateAutoMLJobResponse Source #
Instance details Defined in Amazonka.SageMaker.CreateAutoMLJob type Rep CreateAutoMLJobResponse = D1 ('MetaData "CreateAutoMLJobResponse" "Amazonka.SageMaker.CreateAutoMLJob" "amazonka-sagemaker-2.0-9SyrKZ4KqhsL1qX9u3ILA3" '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 #

Arguments

:: Int	`$sel:httpStatus:CreateAutoMLJobResponse'`
-> Text	`CreateAutoMLJobResponse`
-> CreateAutoMLJobResponse

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.

CreateAutoMLJobResponse, createAutoMLJobResponse_autoMLJobArn - The unique ARN assigned to the AutoML job when it is created.

Response Lenses

createAutoMLJobResponse_httpStatus :: Lens' CreateAutoMLJobResponse Int Source #

The response's http status code.

createAutoMLJobResponse_autoMLJobArn :: Lens' CreateAutoMLJobResponse Text Source #

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