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

Inherits:
Struct
  • Object
show all
Includes:
Aws::Structure
Defined in:
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:



2414
2415
2416
2417
2418
2419
2420
2421
2422
 
# File 'lib/aws-sdk-sagemaker/types.rb', line 2414

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:



2414
2415
2416
2417
2418
2419
2420
2421
2422
 
# File 'lib/aws-sdk-sagemaker/types.rb', line 2414

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:



2414
2415
2416
2417
2418
2419
2420
2421
2422
 
# File 'lib/aws-sdk-sagemaker/types.rb', line 2414

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][1

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][1

for a list of algorithms supported

by ‘HYPERPARAMETER_TUNING` mode.

[1]: docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support

Returns:

  • (String) 


2414
2415
2416
2417
2418
2419
2420
2421
2422
 
# File 'lib/aws-sdk-sagemaker/types.rb', line 2414

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:



2414
2415
2416
2417
2418
2419
2420
2421
2422
 
# File 'lib/aws-sdk-sagemaker/types.rb', line 2414

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