Package software.amazon.awssdk.services.sagemaker.model

Class AutoMLJobConfig

    • 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.

      • 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).

      • 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

      • 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

      • hashCode

        public final int hashCode()
        Overrides:
        hashCode in class Object

      • equals

        public final boolean equals​(Object obj)
        Overrides:
        equals in class Object

      • 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)