sagemaker_create_auto_ml_job: Creates an Autopilot job also referred to as Autopilot...
In paws.machine.learning: 'Amazon Web Services' Machine Learning Services

sagemaker_create_auto_ml_job

R Documentation

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job

Description

Creates an Autopilot job also referred to as Autopilot experiment or AutoML job.

See https://www.paws-r-sdk.com/docs/sagemaker_create_auto_ml_job/ for full documentation.

Usage

sagemaker_create_auto_ml_job(
  AutoMLJobName,
  InputDataConfig,
  OutputDataConfig,
  ProblemType = NULL,
  AutoMLJobObjective = NULL,
  AutoMLJobConfig = NULL,
  RoleArn,
  GenerateCandidateDefinitionsOnly = NULL,
  Tags = NULL,
  ModelDeployConfig = NULL
)

Arguments

`AutoMLJobName`	[required] Identifies an Autopilot job. The name must be unique to your account and is case insensitive.
`InputDataConfig`	[required] 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 HyperParameterTrainingJobDefinition. 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`	[required] Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.
`ProblemType`	Defines the type of supervised learning problem available for the candidates. For more information, see Amazon SageMaker Autopilot problem types.
`AutoMLJobObjective`	Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.
`AutoMLJobConfig`	A collection of settings used to configure an AutoML job.
`RoleArn`	[required] The ARN of the role that is used to access the data.
`GenerateCandidateDefinitionsOnly`	Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.
`Tags`	An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.
`ModelDeployConfig`	Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.