software.amazon.awssdk.services.sagemaker.model

Class AutoMLJobConfig

java.lang.Object

software.amazon.awssdk.services.sagemaker.model.AutoMLJobConfig

All Implemented Interfaces:

Serializable, SdkPojo, ToCopyableBuilder<AutoMLJobConfig.Builder,AutoMLJobConfig>

@Generated(value="software.amazon.awssdk:codegen")
public final class AutoMLJobConfig
extends Object
implements SdkPojo, Serializable, ToCopyableBuilder<AutoMLJobConfig.Builder,AutoMLJobConfig>

A collection of settings used for an AutoML job.

See Also:

Serialized Form

Nested Class Summary

Nested Classes

Modifier and Type	Class and Description
static interface	AutoMLJobConfig.Builder

Method Summary

Modifier and Type	Method and Description
static AutoMLJobConfig.Builder	builder()
AutoMLCandidateGenerationConfig	candidateGenerationConfig() The configuration for generating a candidate for an AutoML job (optional).
AutoMLJobCompletionCriteria	completionCriteria() How long an AutoML job is allowed to run, or how many candidates a job is allowed to generate.
AutoMLDataSplitConfig	dataSplitConfig() The configuration for splitting the input training dataset.
boolean	equals(Object obj)
boolean	equalsBySdkFields(Object obj)
<T> Optional<T>	getValueForField(String fieldName, Class<T> clazz)
int	hashCode()
AutoMLMode	mode() The method that Autopilot uses to train the data.
String	modeAsString() The method that Autopilot uses to train the data.
List<SdkField<?>>	sdkFields()
AutoMLSecurityConfig	securityConfig() The security configuration for traffic encryption or Amazon VPC settings.
static Class<? extends AutoMLJobConfig.Builder>	serializableBuilderClass()
AutoMLJobConfig.Builder	toBuilder()
String	toString() Returns a string representation of this object.

Methods inherited from class java.lang.Object

clone, finalize, getClass, notify, notifyAll, wait, wait, wait

Methods inherited from interface software.amazon.awssdk.utils.builder.ToCopyableBuilder

copy

Method Detail

completionCriteria

public final AutoMLJobCompletionCriteria completionCriteria()

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

Returns:

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

securityConfig

public final AutoMLSecurityConfig securityConfig()

The security configuration for traffic encryption or Amazon VPC settings.

Returns:

The security configuration for traffic encryption or Amazon VPC settings.

dataSplitConfig

public final AutoMLDataSplitConfig dataSplitConfig()

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

Returns:

The configuration for splitting the input training dataset.

Type: AutoMLDataSplitConfig

candidateGenerationConfig

public final AutoMLCandidateGenerationConfig candidateGenerationConfig()

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

Returns:

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

mode

public final AutoMLMode 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. 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.

If the service returns an enum value that is not available in the current SDK version, mode will return AutoMLMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from modeAsString().

Returns:

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.

See Also:

AutoMLMode

modeAsString

public final String modeAsString()

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.

If the service returns an enum value that is not available in the current SDK version, mode will return AutoMLMode.UNKNOWN_TO_SDK_VERSION. The raw value returned by the service is available from modeAsString().

Returns:

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.

See Also:

AutoMLMode

toBuilder

public AutoMLJobConfig.Builder toBuilder()

Specified by:

toBuilder in interface ToCopyableBuilder<AutoMLJobConfig.Builder,AutoMLJobConfig>

builder

public static AutoMLJobConfig.Builder builder()

serializableBuilderClass

public static Class<? extends AutoMLJobConfig.Builder> serializableBuilderClass()

hashCode

public final int hashCode()

Overrides:

hashCode in class Object

equals

public final boolean equals(Object obj)

Overrides:

equals in class Object

equalsBySdkFields

public final boolean equalsBySdkFields(Object obj)

Specified by:

equalsBySdkFields in interface SdkPojo

toString

public final String toString()

Returns a string representation of this object. This is useful for testing and debugging. Sensitive data will be redacted from this string using a placeholder value.

Overrides:

toString in class Object

getValueForField

public final <T> Optional<T> getValueForField(String fieldName,
                                              Class<T> clazz)

sdkFields

public final List<SdkField<?>> sdkFields()

Specified by:

sdkFields in interface SdkPojo