Package software.amazon.awssdk.services.machinelearning.model

Class CreateMlModelRequest

java.lang.Object
software.amazon.awssdk.core.SdkRequest
software.amazon.awssdk.awscore.AwsRequest
software.amazon.awssdk.services.machinelearning.model.MachineLearningRequest
software.amazon.awssdk.services.machinelearning.model.CreateMlModelRequest
All Implemented Interfaces:
SdkPojo, ToCopyableBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>
@Generated("software.amazon.awssdk:codegen") public final class CreateMlModelRequest extends MachineLearningRequest implements ToCopyableBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>

  • Method Details

    • mlModelId

      public final String mlModelId()

      A user-supplied ID that uniquely identifies the MLModel.

      Returns:
      A user-supplied ID that uniquely identifies the MLModel.

    • mlModelName

      public final String mlModelName()

      A user-supplied name or description of the MLModel.

      Returns:
      A user-supplied name or description of the MLModel.

    • mlModelType

      public final MLModelType mlModelType()

      The category of supervised learning that this MLModel will address. Choose from the following types:

      • Choose REGRESSION if the MLModel will be used to predict a numeric value.

      • Choose BINARY if the MLModel result has two possible values.

      • Choose MULTICLASS if the MLModel result has a limited number of values.

      For more information, see the Amazon Machine Learning Developer Guide.

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

      Returns:
      The category of supervised learning that this MLModel will address. Choose from the following types:

      • Choose REGRESSION if the MLModel will be used to predict a numeric value.

      • Choose BINARY if the MLModel result has two possible values.

      • Choose MULTICLASS if the MLModel result has a limited number of values.

      For more information, see the Amazon Machine Learning Developer Guide.

      See Also:

    • mlModelTypeAsString

      public final String mlModelTypeAsString()

      The category of supervised learning that this MLModel will address. Choose from the following types:

      • Choose REGRESSION if the MLModel will be used to predict a numeric value.

      • Choose BINARY if the MLModel result has two possible values.

      • Choose MULTICLASS if the MLModel result has a limited number of values.

      For more information, see the Amazon Machine Learning Developer Guide.

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

      Returns:
      The category of supervised learning that this MLModel will address. Choose from the following types:

      • Choose REGRESSION if the MLModel will be used to predict a numeric value.

      • Choose BINARY if the MLModel result has two possible values.

      • Choose MULTICLASS if the MLModel result has a limited number of values.

      For more information, see the Amazon Machine Learning Developer Guide.

      See Also:

    • hasParameters

      public final boolean hasParameters()
      For responses, this returns true if the service returned a value for the Parameters property. This DOES NOT check that the value is non-empty (for which, you should check the isEmpty() method on the property). This is useful because the SDK will never return a null collection or map, but you may need to differentiate between the service returning nothing (or null) and the service returning an empty collection or map. For requests, this returns true if a value for the property was specified in the request builder, and false if a value was not specified.

    • parameters

      public final Map<String,String> parameters()

      A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

      The following is the current set of training parameters:

      • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

        The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

      • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

      • sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.

      • sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

      Attempts to modify the collection returned by this method will result in an UnsupportedOperationException.

      This method will never return null. If you would like to know whether the service returned this field (so that you can differentiate between null and empty), you can use the hasParameters() method.

      Returns:
      A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

      The following is the current set of training parameters:

      • sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.

        The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.

      • sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.

      • sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model's ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We strongly recommend that you shuffle your data.

      • sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can't be used when L2 is specified. Use this parameter sparingly.

      • sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.

        The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can't be used when L1 is specified. Use this parameter sparingly.

    • trainingDataSourceId

      public final String trainingDataSourceId()

      The DataSource that points to the training data.

      Returns:
      The DataSource that points to the training data.

    • recipe

      public final String recipe()

      The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

      Returns:
      The data recipe for creating the MLModel. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

    • recipeUri

      public final String recipeUri()

      The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

      Returns:
      The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don't specify a recipe or its URI, Amazon ML creates a default.

    • toBuilder

      public CreateMlModelRequest.Builder toBuilder()
      Specified by:
      toBuilder in interface ToCopyableBuilder<CreateMlModelRequest.Builder,CreateMlModelRequest>
      Specified by:
      toBuilder in class MachineLearningRequest

    • builder

      public static CreateMlModelRequest.Builder builder()

    • serializableBuilderClass

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

    • hashCode

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

    • equals

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

    • 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)
      Overrides:
      getValueForField in class SdkRequest

    • sdkFields

      public final List<SdkField<?>> sdkFields()
      Specified by:
      sdkFields in interface SdkPojo

    • sdkFieldNameToField

      public final Map<String,SdkField<?>> sdkFieldNameToField()
      Specified by:
      sdkFieldNameToField in interface SdkPojo