AutoMLJobConfig — AWS SDK for Ruby V3 (original) (raw)

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

Inherits:

Struct

• Object
• Struct
• Aws::SageMaker::Types::AutoMLJobConfig show all

Defined in:

gems/aws-sdk-sagemaker/lib/aws-sdk-sagemaker/types.rb

Overview

A collection of settings used for an AutoML job.

Constant Summarycollapse

SENSITIVE =

[]

Instance Attribute Summary collapse

• #candidate_generation_config ⇒ Types::AutoMLCandidateGenerationConfig
  The configuration for generating a candidate for an AutoML job (optional).
• #completion_criteria ⇒ Types::AutoMLJobCompletionCriteria
  How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
• #data_split_config ⇒ Types::AutoMLDataSplitConfig
  The configuration for splitting the input training dataset.
• #mode ⇒ String
  The method that Autopilot uses to train the data.
• #security_config ⇒ Types::AutoMLSecurityConfig
  The security configuration for traffic encryption or Amazon VPC settings.

Instance Attribute Details

#candidate_generation_config ⇒ Types::AutoMLCandidateGenerationConfig

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

#completion_criteria ⇒ Types::AutoMLJobCompletionCriteria

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

#data_split_config ⇒ Types::AutoMLDataSplitConfig

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

#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, andHYPERPARAMETER_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. SeeAutopilot 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. SeeAutopilot algorithm support for a list of algorithms supported by HYPERPARAMETER_TUNING mode.

#security_config ⇒ Types::AutoMLSecurityConfig

The security configuration for traffic encryption or Amazon VPC settings.