# AutoMLJobConfig
<a name="API_AutoMLJobConfig"></a>

A collection of settings used for an AutoML job.

## Contents
<a name="API_AutoMLJobConfig_Contents"></a>

 ** CandidateGenerationConfig **   <a name="sagemaker-Type-AutoMLJobConfig-CandidateGenerationConfig"></a>
The configuration for generating a candidate for an AutoML job (optional).   
Type: [AutoMLCandidateGenerationConfig](API_AutoMLCandidateGenerationConfig.md) object  
Required: No

 ** CompletionCriteria **   <a name="sagemaker-Type-AutoMLJobConfig-CompletionCriteria"></a>
How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.  
Type: [AutoMLJobCompletionCriteria](API_AutoMLJobCompletionCriteria.md) object  
Required: No

 ** DataSplitConfig **   <a name="sagemaker-Type-AutoMLJobConfig-DataSplitConfig"></a>
The configuration for splitting the input training dataset.  
Type: AutoMLDataSplitConfig  
Type: [AutoMLDataSplitConfig](API_AutoMLDataSplitConfig.md) object  
Required: No

 ** Mode **   <a name="sagemaker-Type-AutoMLJobConfig-Mode"></a>
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](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#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](https://docs.aws.amazon.com/sagemaker/latest/dg/autopilot-model-support-validation.html#autopilot-algorithm-support) for a list of algorithms supported by `HYPERPARAMETER_TUNING` mode.  
Type: String  
Valid Values: `AUTO | ENSEMBLING | HYPERPARAMETER_TUNING`   
Required: No

 ** SecurityConfig **   <a name="sagemaker-Type-AutoMLJobConfig-SecurityConfig"></a>
The security configuration for traffic encryption or Amazon VPC settings.  
Type: [AutoMLSecurityConfig](API_AutoMLSecurityConfig.md) object  
Required: No

## See Also
<a name="API_AutoMLJobConfig_SeeAlso"></a>

For more information about using this API in one of the language-specific AWS SDKs, see the following:
+  [AWS SDK for C\$1\$1](https://docs.aws.amazon.com/goto/SdkForCpp/sagemaker-2017-07-24/AutoMLJobConfig) 
+  [AWS SDK for Java V2](https://docs.aws.amazon.com/goto/SdkForJavaV2/sagemaker-2017-07-24/AutoMLJobConfig) 
+  [AWS SDK for Ruby V3](https://docs.aws.amazon.com/goto/SdkForRubyV3/sagemaker-2017-07-24/AutoMLJobConfig)