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AWS::SageMaker::AutoMLJob AutoMLJobConfig
A collection of settings used for an AutoML job.
Syntax
To declare this entity in your Amazon CloudFormation template, use the following syntax:
JSON
{ "CandidateGenerationConfig" :AutoMLCandidateGenerationConfig, "CompletionCriteria" :AutoMLJobCompletionCriteria, "DataSplitConfig" :AutoMLDataSplitConfig, "Mode" :String, "SecurityConfig" :AutoMLSecurityConfig}
YAML
CandidateGenerationConfig:AutoMLCandidateGenerationConfigCompletionCriteria:AutoMLJobCompletionCriteriaDataSplitConfig:AutoMLDataSplitConfigMode:StringSecurityConfig:AutoMLSecurityConfig
Properties
CandidateGenerationConfig-
The configuration for generating a candidate for an AutoML job (optional).
Required: No
Type: AutoMLCandidateGenerationConfig
Update requires: Replacement
CompletionCriteria-
How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
Required: No
Type: AutoMLJobCompletionCriteria
Update requires: Replacement
DataSplitConfig-
The configuration for splitting the input training dataset.
Type: AutoMLDataSplitConfig
Required: No
Type: AutoMLDataSplitConfig
Update requires: Replacement
Mode-
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. InAUTOmode, Autopilot choosesENSEMBLINGfor datasets smaller than 100 MB, andHYPERPARAMETER_TUNINGfor larger ones.The
ENSEMBLINGmode 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 byENSEMBLINGmode.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 byHYPERPARAMETER_TUNINGmode.Required: No
Type: String
Allowed values:
AUTO | ENSEMBLING | HYPERPARAMETER_TUNINGUpdate requires: Replacement
SecurityConfig-
The security configuration for traffic encryption or Amazon VPC settings.
Required: No
Type: AutoMLSecurityConfig
Update requires: Replacement