Class: Aws::SageMaker::Types::AutoMLJobConfig

Inherits:
Struct
  • Object
show all
Defined in:
gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb

Overview

A collection of settings used for an AutoML job.

See Also:

Constant Summary collapse

SENSITIVE = 
[]

Instance Attribute Summary collapse

Instance Attribute Details

#candidate_generation_config ⇒ Types::AutoMLCandidateGenerationConfig

The configuration for generating a candidate for an AutoML job (optional).

Returns:



2407
2408
2409
2410
2411
2412
2413
2414
2415
 
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2407

class AutoMLJobConfig < Struct.new(
  :completion_criteria,
  :security_config,
  :candidate_generation_config,
  :data_split_config,
  :mode)
  SENSITIVE = []
  include Aws::Structure
end

#completion_criteria ⇒ Types::AutoMLJobCompletionCriteria

How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.

Returns:



2407
2408
2409
2410
2411
2412
2413
2414
2415
 
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2407

class AutoMLJobConfig < Struct.new(
  :completion_criteria,
  :security_config,
  :candidate_generation_config,
  :data_split_config,
  :mode)
  SENSITIVE = []
  include Aws::Structure
end

#data_split_config ⇒ Types::AutoMLDataSplitConfig

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

Returns:



2407
2408
2409
2410
2411
2412
2413
2414
2415
 
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2407

class AutoMLJobConfig < Struct.new(
  :completion_criteria,
  :security_config,
  :candidate_generation_config,
  :data_split_config,
  :mode)
  SENSITIVE = []
  include Aws::Structure
end

#mode ⇒ String

The method that Autopilot uses to train the data. You can either specify the mode manually or let Autopilot choose for you based on the dataset size by selecting AUTO. In AUTO mode, Autopilot chooses ENSEMBLING for datasets smaller than 100 MB, and HYPERPARAMETER_TUNING for larger ones.

The ENSEMBLING mode uses a multi-stack ensemble model to predict classification and regression tasks directly from your dataset. This machine learning mode combines several base models to produce an optimal predictive model. It then uses a stacking ensemble method to combine predictions from contributing members. A multi-stack ensemble model can provide better performance over a single model by combining the predictive capabilities of multiple models. See Autopilot algorithm support for a list of algorithms supported by ENSEMBLING mode.

The HYPERPARAMETER_TUNING (HPO) mode uses the best hyperparameters to train the best version of a model. HPO automatically selects an algorithm for the type of problem you want to solve. Then HPO finds the best hyperparameters according to your objective metric. See Autopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

Returns:

  • (String) 


2407
2408
2409
2410
2411
2412
2413
2414
2415
 
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2407

class AutoMLJobConfig < Struct.new(
  :completion_criteria,
  :security_config,
  :candidate_generation_config,
  :data_split_config,
  :mode)
  SENSITIVE = []
  include Aws::Structure
end

#security_config ⇒ Types::AutoMLSecurityConfig

The security configuration for traffic encryption or Amazon VPC settings.

Returns:



2407
2408
2409
2410
2411
2412
2413
2414
2415
 
# File 'gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb', line 2407

class AutoMLJobConfig < Struct.new(
  :completion_criteria,
  :security_config,
  :candidate_generation_config,
  :data_split_config,
  :mode)
  SENSITIVE = []
  include Aws::Structure
end