Class: Aws::SageMaker::Types::CreateAutoMLJobRequest

Inherits:

Struct

• Object
• Struct
• Aws::SageMaker::Types::CreateAutoMLJobRequest

Includes:

Aws::Structure

Defined in:

lib/aws-sdk-sagemaker/types.rb

Overview

See Also:

• AWS API Documentation

Constant Summary

SENSITIVE =

[]

Instance Attribute Summary

• #auto_ml_job_config ⇒ Types::AutoMLJobConfig

  A collection of settings used to configure an AutoML job.

• #auto_ml_job_name ⇒ String

  Identifies an Autopilot job.

• #auto_ml_job_objective ⇒ Types::AutoMLJobObjective

  Specifies a metric to minimize or maximize as the objective of a job.

• #generate_candidate_definitions_only ⇒ Boolean

  Generates possible candidates without training the models.

• #input_data_config ⇒ Array<Types::AutoMLChannel>

  An array of channel objects that describes the input data and its location.

• #model_deploy_config ⇒ Types::ModelDeployConfig

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

• #output_data_config ⇒ Types::AutoMLOutputDataConfig

  Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.

• #problem_type ⇒ String

  Defines the type of supervised learning problem available for the candidates.

• #role_arn ⇒ String

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

• #tags ⇒ Array<Types::Tag>

  An array of key-value pairs.

Instance Attribute Details

#auto_ml_job_config ⇒ Types::AutoMLJobConfig

A collection of settings used to configure an AutoML job.

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_job_name ⇒ String

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

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#auto_ml_job_objective ⇒ 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.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_AutoMLJobObjective.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#generate_candidate_definitions_only ⇒ Boolean

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

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#input_data_config ⇒ Array<Types::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 [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.

[1]: docs.aws.amazon.com/sagemaker/latest/APIReference/API_HyperParameterTrainingJobDefinition.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#model_deploy_config ⇒ Types::ModelDeployConfig

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

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#output_data_config ⇒ Types::AutoMLOutputDataConfig

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

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#problem_type ⇒ String

Defines the type of supervised learning problem available for the candidates. For more information, see [ SageMaker Autopilot problem types].

[1]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-datasets-problem-types.html#autopilot-problem-types

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#role_arn ⇒ String

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

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end

#tags ⇒ Array<Types::Tag>

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.

[1]: docs.aws.amazon.com/general/latest/gr/aws_tagging.html

# File 'lib/aws-sdk-sagemaker/types.rb', line 8046

class CreateAutoMLJobRequest < Struct.new(
  :auto_ml_job_name,
  :input_data_config,
  :output_data_config,
  :problem_type,
  :auto_ml_job_objective,
  :auto_ml_job_config,
  :role_arn,
  :generate_candidate_definitions_only,
  :tags,
  :model_deploy_config)
  SENSITIVE = []
  include Aws::Structure
end