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amazonka-sagemaker-2.0: gen/Amazonka/SageMaker/CreateAutoMLJob.hs

{-# LANGUAGE DeriveGeneric #-}
{-# LANGUAGE DuplicateRecordFields #-}
{-# LANGUAGE NamedFieldPuns #-}
{-# LANGUAGE OverloadedStrings #-}
{-# LANGUAGE RecordWildCards #-}
{-# LANGUAGE StrictData #-}
{-# LANGUAGE TypeFamilies #-}
{-# LANGUAGE NoImplicitPrelude #-}
{-# OPTIONS_GHC -fno-warn-unused-binds #-}
{-# OPTIONS_GHC -fno-warn-unused-imports #-}
{-# OPTIONS_GHC -fno-warn-unused-matches #-}

-- Derived from AWS service descriptions, licensed under Apache 2.0.

-- |
-- Module      : Amazonka.SageMaker.CreateAutoMLJob
-- Copyright   : (c) 2013-2023 Brendan Hay
-- License     : Mozilla Public License, v. 2.0.
-- Maintainer  : Brendan Hay
-- Stability   : auto-generated
-- Portability : non-portable (GHC extensions)
--
-- 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
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development.html Automate Model Development with Amazon SageMaker Autopilot>.
module Amazonka.SageMaker.CreateAutoMLJob
  ( -- * Creating a Request
    CreateAutoMLJob (..),
    newCreateAutoMLJob,

    -- * Request Lenses
    createAutoMLJob_autoMLJobConfig,
    createAutoMLJob_autoMLJobObjective,
    createAutoMLJob_generateCandidateDefinitionsOnly,
    createAutoMLJob_modelDeployConfig,
    createAutoMLJob_problemType,
    createAutoMLJob_tags,
    createAutoMLJob_autoMLJobName,
    createAutoMLJob_inputDataConfig,
    createAutoMLJob_outputDataConfig,
    createAutoMLJob_roleArn,

    -- * Destructuring the Response
    CreateAutoMLJobResponse (..),
    newCreateAutoMLJobResponse,

    -- * Response Lenses
    createAutoMLJobResponse_httpStatus,
    createAutoMLJobResponse_autoMLJobArn,
  )
where

import qualified Amazonka.Core as Core
import qualified Amazonka.Core.Lens.Internal as Lens
import qualified Amazonka.Data as Data
import qualified Amazonka.Prelude as Prelude
import qualified Amazonka.Request as Request
import qualified Amazonka.Response as Response
import Amazonka.SageMaker.Types

-- | /See:/ 'newCreateAutoMLJob' smart constructor.
data CreateAutoMLJob = CreateAutoMLJob'
  { -- | A collection of settings used to configure an AutoML job.
    autoMLJobConfig :: Prelude.Maybe AutoMLJobConfig,
    -- | 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.
    autoMLJobObjective :: Prelude.Maybe AutoMLJobObjective,
    -- | Generates possible candidates without training the models. A candidate
    -- is a combination of data preprocessors, algorithms, and algorithm
    -- parameter settings.
    generateCandidateDefinitionsOnly :: Prelude.Maybe Prelude.Bool,
    -- | Specifies how to generate the endpoint name for an automatic one-click
    -- Autopilot model deployment.
    modelDeployConfig :: Prelude.Maybe ModelDeployConfig,
    -- | Defines the type of supervised learning available for the candidates.
    -- For more information, see
    -- <https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-problem-types.html Amazon SageMaker Autopilot problem types and algorithm support>.
    problemType :: Prelude.Maybe ProblemType,
    -- | Each tag consists of a key and an optional value. Tag keys must be
    -- unique per resource.
    tags :: Prelude.Maybe [Tag],
    -- | Identifies an Autopilot job. The name must be unique to your account and
    -- is case-insensitive.
    autoMLJobName :: Prelude.Text,
    -- | 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.
    inputDataConfig :: Prelude.NonEmpty AutoMLChannel,
    -- | Provides information about encryption and the Amazon S3 output path
    -- needed to store artifacts from an AutoML job. Format(s) supported: CSV.
    outputDataConfig :: AutoMLOutputDataConfig,
    -- | The ARN of the role that is used to access the data.
    roleArn :: Prelude.Text
  }
  deriving (Prelude.Eq, Prelude.Read, Prelude.Show, Prelude.Generic)

-- |
-- Create a value of 'CreateAutoMLJob' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'autoMLJobConfig', 'createAutoMLJob_autoMLJobConfig' - A collection of settings used to configure an AutoML job.
--
-- 'autoMLJobObjective', '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.
--
-- 'generateCandidateDefinitionsOnly', 'createAutoMLJob_generateCandidateDefinitionsOnly' - Generates possible candidates without training the models. A candidate
-- is a combination of data preprocessors, algorithms, and algorithm
-- parameter settings.
--
-- 'modelDeployConfig', 'createAutoMLJob_modelDeployConfig' - Specifies how to generate the endpoint name for an automatic one-click
-- Autopilot model deployment.
--
-- 'problemType', 'createAutoMLJob_problemType' - Defines the type of supervised learning available for the candidates.
-- For more information, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-problem-types.html Amazon SageMaker Autopilot problem types and algorithm support>.
--
-- 'tags', 'createAutoMLJob_tags' - Each tag consists of a key and an optional value. Tag keys must be
-- unique per resource.
--
-- 'autoMLJobName', 'createAutoMLJob_autoMLJobName' - Identifies an Autopilot job. The name must be unique to your account and
-- is case-insensitive.
--
-- 'inputDataConfig', '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.
--
-- 'outputDataConfig', 'createAutoMLJob_outputDataConfig' - Provides information about encryption and the Amazon S3 output path
-- needed to store artifacts from an AutoML job. Format(s) supported: CSV.
--
-- 'roleArn', 'createAutoMLJob_roleArn' - The ARN of the role that is used to access the data.
newCreateAutoMLJob ::
  -- | 'autoMLJobName'
  Prelude.Text ->
  -- | 'inputDataConfig'
  Prelude.NonEmpty AutoMLChannel ->
  -- | 'outputDataConfig'
  AutoMLOutputDataConfig ->
  -- | 'roleArn'
  Prelude.Text ->
  CreateAutoMLJob
newCreateAutoMLJob
  pAutoMLJobName_
  pInputDataConfig_
  pOutputDataConfig_
  pRoleArn_ =
    CreateAutoMLJob'
      { autoMLJobConfig = Prelude.Nothing,
        autoMLJobObjective = Prelude.Nothing,
        generateCandidateDefinitionsOnly = Prelude.Nothing,
        modelDeployConfig = Prelude.Nothing,
        problemType = Prelude.Nothing,
        tags = Prelude.Nothing,
        autoMLJobName = pAutoMLJobName_,
        inputDataConfig =
          Lens.coerced Lens.# pInputDataConfig_,
        outputDataConfig = pOutputDataConfig_,
        roleArn = pRoleArn_
      }

-- | A collection of settings used to configure an AutoML job.
createAutoMLJob_autoMLJobConfig :: Lens.Lens' CreateAutoMLJob (Prelude.Maybe AutoMLJobConfig)
createAutoMLJob_autoMLJobConfig = Lens.lens (\CreateAutoMLJob' {autoMLJobConfig} -> autoMLJobConfig) (\s@CreateAutoMLJob' {} a -> s {autoMLJobConfig = a} :: CreateAutoMLJob)

-- | 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_autoMLJobObjective :: Lens.Lens' CreateAutoMLJob (Prelude.Maybe AutoMLJobObjective)
createAutoMLJob_autoMLJobObjective = Lens.lens (\CreateAutoMLJob' {autoMLJobObjective} -> autoMLJobObjective) (\s@CreateAutoMLJob' {} a -> s {autoMLJobObjective = a} :: CreateAutoMLJob)

-- | Generates possible candidates without training the models. A candidate
-- is a combination of data preprocessors, algorithms, and algorithm
-- parameter settings.
createAutoMLJob_generateCandidateDefinitionsOnly :: Lens.Lens' CreateAutoMLJob (Prelude.Maybe Prelude.Bool)
createAutoMLJob_generateCandidateDefinitionsOnly = Lens.lens (\CreateAutoMLJob' {generateCandidateDefinitionsOnly} -> generateCandidateDefinitionsOnly) (\s@CreateAutoMLJob' {} a -> s {generateCandidateDefinitionsOnly = a} :: CreateAutoMLJob)

-- | Specifies how to generate the endpoint name for an automatic one-click
-- Autopilot model deployment.
createAutoMLJob_modelDeployConfig :: Lens.Lens' CreateAutoMLJob (Prelude.Maybe ModelDeployConfig)
createAutoMLJob_modelDeployConfig = Lens.lens (\CreateAutoMLJob' {modelDeployConfig} -> modelDeployConfig) (\s@CreateAutoMLJob' {} a -> s {modelDeployConfig = a} :: CreateAutoMLJob)

-- | Defines the type of supervised learning available for the candidates.
-- For more information, see
-- <https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-automate-model-development-problem-types.html Amazon SageMaker Autopilot problem types and algorithm support>.
createAutoMLJob_problemType :: Lens.Lens' CreateAutoMLJob (Prelude.Maybe ProblemType)
createAutoMLJob_problemType = Lens.lens (\CreateAutoMLJob' {problemType} -> problemType) (\s@CreateAutoMLJob' {} a -> s {problemType = a} :: CreateAutoMLJob)

-- | Each tag consists of a key and an optional value. Tag keys must be
-- unique per resource.
createAutoMLJob_tags :: Lens.Lens' CreateAutoMLJob (Prelude.Maybe [Tag])
createAutoMLJob_tags = Lens.lens (\CreateAutoMLJob' {tags} -> tags) (\s@CreateAutoMLJob' {} a -> s {tags = a} :: CreateAutoMLJob) Prelude.. Lens.mapping Lens.coerced

-- | Identifies an Autopilot job. The name must be unique to your account and
-- is case-insensitive.
createAutoMLJob_autoMLJobName :: Lens.Lens' CreateAutoMLJob Prelude.Text
createAutoMLJob_autoMLJobName = Lens.lens (\CreateAutoMLJob' {autoMLJobName} -> autoMLJobName) (\s@CreateAutoMLJob' {} a -> s {autoMLJobName = a} :: CreateAutoMLJob)

-- | 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_inputDataConfig :: Lens.Lens' CreateAutoMLJob (Prelude.NonEmpty AutoMLChannel)
createAutoMLJob_inputDataConfig = Lens.lens (\CreateAutoMLJob' {inputDataConfig} -> inputDataConfig) (\s@CreateAutoMLJob' {} a -> s {inputDataConfig = a} :: CreateAutoMLJob) Prelude.. Lens.coerced

-- | Provides information about encryption and the Amazon S3 output path
-- needed to store artifacts from an AutoML job. Format(s) supported: CSV.
createAutoMLJob_outputDataConfig :: Lens.Lens' CreateAutoMLJob AutoMLOutputDataConfig
createAutoMLJob_outputDataConfig = Lens.lens (\CreateAutoMLJob' {outputDataConfig} -> outputDataConfig) (\s@CreateAutoMLJob' {} a -> s {outputDataConfig = a} :: CreateAutoMLJob)

-- | The ARN of the role that is used to access the data.
createAutoMLJob_roleArn :: Lens.Lens' CreateAutoMLJob Prelude.Text
createAutoMLJob_roleArn = Lens.lens (\CreateAutoMLJob' {roleArn} -> roleArn) (\s@CreateAutoMLJob' {} a -> s {roleArn = a} :: CreateAutoMLJob)

instance Core.AWSRequest CreateAutoMLJob where
  type
    AWSResponse CreateAutoMLJob =
      CreateAutoMLJobResponse
  request overrides =
    Request.postJSON (overrides defaultService)
  response =
    Response.receiveJSON
      ( \s h x ->
          CreateAutoMLJobResponse'
            Prelude.<$> (Prelude.pure (Prelude.fromEnum s))
            Prelude.<*> (x Data..:> "AutoMLJobArn")
      )

instance Prelude.Hashable CreateAutoMLJob where
  hashWithSalt _salt CreateAutoMLJob' {..} =
    _salt
      `Prelude.hashWithSalt` autoMLJobConfig
      `Prelude.hashWithSalt` autoMLJobObjective
      `Prelude.hashWithSalt` generateCandidateDefinitionsOnly
      `Prelude.hashWithSalt` modelDeployConfig
      `Prelude.hashWithSalt` problemType
      `Prelude.hashWithSalt` tags
      `Prelude.hashWithSalt` autoMLJobName
      `Prelude.hashWithSalt` inputDataConfig
      `Prelude.hashWithSalt` outputDataConfig
      `Prelude.hashWithSalt` roleArn

instance Prelude.NFData CreateAutoMLJob where
  rnf CreateAutoMLJob' {..} =
    Prelude.rnf autoMLJobConfig
      `Prelude.seq` Prelude.rnf autoMLJobObjective
      `Prelude.seq` Prelude.rnf generateCandidateDefinitionsOnly
      `Prelude.seq` Prelude.rnf modelDeployConfig
      `Prelude.seq` Prelude.rnf problemType
      `Prelude.seq` Prelude.rnf tags
      `Prelude.seq` Prelude.rnf autoMLJobName
      `Prelude.seq` Prelude.rnf inputDataConfig
      `Prelude.seq` Prelude.rnf outputDataConfig
      `Prelude.seq` Prelude.rnf roleArn

instance Data.ToHeaders CreateAutoMLJob where
  toHeaders =
    Prelude.const
      ( Prelude.mconcat
          [ "X-Amz-Target"
              Data.=# ("SageMaker.CreateAutoMLJob" :: Prelude.ByteString),
            "Content-Type"
              Data.=# ( "application/x-amz-json-1.1" ::
                          Prelude.ByteString
                      )
          ]
      )

instance Data.ToJSON CreateAutoMLJob where
  toJSON CreateAutoMLJob' {..} =
    Data.object
      ( Prelude.catMaybes
          [ ("AutoMLJobConfig" Data..=)
              Prelude.<$> autoMLJobConfig,
            ("AutoMLJobObjective" Data..=)
              Prelude.<$> autoMLJobObjective,
            ("GenerateCandidateDefinitionsOnly" Data..=)
              Prelude.<$> generateCandidateDefinitionsOnly,
            ("ModelDeployConfig" Data..=)
              Prelude.<$> modelDeployConfig,
            ("ProblemType" Data..=) Prelude.<$> problemType,
            ("Tags" Data..=) Prelude.<$> tags,
            Prelude.Just ("AutoMLJobName" Data..= autoMLJobName),
            Prelude.Just
              ("InputDataConfig" Data..= inputDataConfig),
            Prelude.Just
              ("OutputDataConfig" Data..= outputDataConfig),
            Prelude.Just ("RoleArn" Data..= roleArn)
          ]
      )

instance Data.ToPath CreateAutoMLJob where
  toPath = Prelude.const "/"

instance Data.ToQuery CreateAutoMLJob where
  toQuery = Prelude.const Prelude.mempty

-- | /See:/ 'newCreateAutoMLJobResponse' smart constructor.
data CreateAutoMLJobResponse = CreateAutoMLJobResponse'
  { -- | The response's http status code.
    httpStatus :: Prelude.Int,
    -- | The unique ARN assigned to the AutoML job when it is created.
    autoMLJobArn :: Prelude.Text
  }
  deriving (Prelude.Eq, Prelude.Read, Prelude.Show, Prelude.Generic)

-- |
-- Create a value of 'CreateAutoMLJobResponse' with all optional fields omitted.
--
-- Use <https://hackage.haskell.org/package/generic-lens generic-lens> or <https://hackage.haskell.org/package/optics optics> to modify other optional fields.
--
-- The following record fields are available, with the corresponding lenses provided
-- for backwards compatibility:
--
-- 'httpStatus', 'createAutoMLJobResponse_httpStatus' - The response's http status code.
--
-- 'autoMLJobArn', 'createAutoMLJobResponse_autoMLJobArn' - The unique ARN assigned to the AutoML job when it is created.
newCreateAutoMLJobResponse ::
  -- | 'httpStatus'
  Prelude.Int ->
  -- | 'autoMLJobArn'
  Prelude.Text ->
  CreateAutoMLJobResponse
newCreateAutoMLJobResponse
  pHttpStatus_
  pAutoMLJobArn_ =
    CreateAutoMLJobResponse'
      { httpStatus = pHttpStatus_,
        autoMLJobArn = pAutoMLJobArn_
      }

-- | The response's http status code.
createAutoMLJobResponse_httpStatus :: Lens.Lens' CreateAutoMLJobResponse Prelude.Int
createAutoMLJobResponse_httpStatus = Lens.lens (\CreateAutoMLJobResponse' {httpStatus} -> httpStatus) (\s@CreateAutoMLJobResponse' {} a -> s {httpStatus = a} :: CreateAutoMLJobResponse)

-- | The unique ARN assigned to the AutoML job when it is created.
createAutoMLJobResponse_autoMLJobArn :: Lens.Lens' CreateAutoMLJobResponse Prelude.Text
createAutoMLJobResponse_autoMLJobArn = Lens.lens (\CreateAutoMLJobResponse' {autoMLJobArn} -> autoMLJobArn) (\s@CreateAutoMLJobResponse' {} a -> s {autoMLJobArn = a} :: CreateAutoMLJobResponse)

instance Prelude.NFData CreateAutoMLJobResponse where
  rnf CreateAutoMLJobResponse' {..} =
    Prelude.rnf httpStatus
      `Prelude.seq` Prelude.rnf autoMLJobArn