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

Source: R/sagemaker_operations.R
sagemaker_create_auto_ml_job.Rd

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 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.