All Implemented Interfaces:

HandlerContextAware, ReadLimitInfo, Serializable, Cloneable

@Generated(value="com.amazonaws:aws-java-sdk-code-generator")
public class CreateAutoMLJobRequest
extends AmazonWebServiceRequest
implements Serializable, Cloneable 

See Also:

AWS API Documentation, Serialized Form

• Field Summary

  • Fields inherited from class com.amazonaws.AmazonWebServiceRequest

    NOOP

• Constructor Summary

  Constructors

  Constructor and Description
  CreateAutoMLJobRequest ()

• Method Summary

  All Methods

  Modifier and Type	Method and Description
  CreateAutoMLJobRequest	clone () Creates a shallow clone of this object for all fields except the handler context.
  boolean	equals (Object obj)
  AutoMLJobConfig	getAutoMLJobConfig () A collection of settings used to configure an AutoML job.
  String	getAutoMLJobName () Identifies an Autopilot job.
  AutoMLJobObjective	getAutoMLJobObjective () Specifies a metric to minimize or maximize as the objective of a job.
  Boolean	getGenerateCandidateDefinitionsOnly () Generates possible candidates without training the models.
  List<AutoMLChannel>	getInputDataConfig () An array of channel objects that describes the input data and its location.
  ModelDeployConfig	getModelDeployConfig () Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
  AutoMLOutputDataConfig	getOutputDataConfig () Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
  String	getProblemType () Defines the type of supervised learning problem available for the candidates.
  String	getRoleArn () The ARN of the role that is used to access the data.
  List<Tag>	getTags () An array of key-value pairs.
  int	hashCode ()
  Boolean	isGenerateCandidateDefinitionsOnly () Generates possible candidates without training the models.
  void	setAutoMLJobConfig (AutoMLJobConfig autoMLJobConfig) A collection of settings used to configure an AutoML job.
  void	setAutoMLJobName (String autoMLJobName) Identifies an Autopilot job.
  void	setAutoMLJobObjective (AutoMLJobObjective autoMLJobObjective) Specifies a metric to minimize or maximize as the objective of a job.
  void	setGenerateCandidateDefinitionsOnly (Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models.
  void	setInputDataConfig (Collection<AutoMLChannel> inputDataConfig) An array of channel objects that describes the input data and its location.
  void	setModelDeployConfig (ModelDeployConfig modelDeployConfig) Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
  void	setOutputDataConfig (AutoMLOutputDataConfig outputDataConfig) Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
  void	setProblemType (String problemType) Defines the type of supervised learning problem available for the candidates.
  void	setRoleArn (String roleArn) The ARN of the role that is used to access the data.
  void	setTags (Collection<Tag> tags) An array of key-value pairs.
  String	toString () Returns a string representation of this object.
  CreateAutoMLJobRequest	withAutoMLJobConfig (AutoMLJobConfig autoMLJobConfig) A collection of settings used to configure an AutoML job.
  CreateAutoMLJobRequest	withAutoMLJobName (String autoMLJobName) Identifies an Autopilot job.
  CreateAutoMLJobRequest	withAutoMLJobObjective (AutoMLJobObjective autoMLJobObjective) Specifies a metric to minimize or maximize as the objective of a job.
  CreateAutoMLJobRequest	withGenerateCandidateDefinitionsOnly (Boolean generateCandidateDefinitionsOnly) Generates possible candidates without training the models.
  CreateAutoMLJobRequest	withInputDataConfig (AutoMLChannel... inputDataConfig) An array of channel objects that describes the input data and its location.
  CreateAutoMLJobRequest	withInputDataConfig (Collection<AutoMLChannel> inputDataConfig) An array of channel objects that describes the input data and its location.
  CreateAutoMLJobRequest	withModelDeployConfig (ModelDeployConfig modelDeployConfig) Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.
  CreateAutoMLJobRequest	withOutputDataConfig (AutoMLOutputDataConfig outputDataConfig) Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job.
  CreateAutoMLJobRequest	withProblemType (ProblemType problemType) Defines the type of supervised learning problem available for the candidates.
  CreateAutoMLJobRequest	withProblemType (String problemType) Defines the type of supervised learning problem available for the candidates.
  CreateAutoMLJobRequest	withRoleArn (String roleArn) The ARN of the role that is used to access the data.
  CreateAutoMLJobRequest	withTags (Collection<Tag> tags) An array of key-value pairs.
  CreateAutoMLJobRequest	withTags (Tag... tags) An array of key-value pairs.

  • Methods inherited from class com.amazonaws.AmazonWebServiceRequest

    addHandlerContext, getCloneRoot, getCloneSource, getCustomQueryParameters, getCustomRequestHeaders, getGeneralProgressListener, getHandlerContext, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestCredentialsProvider, getRequestMetricCollector, getSdkClientExecutionTimeout, getSdkRequestTimeout, putCustomQueryParameter, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestCredentialsProvider, setRequestMetricCollector, setSdkClientExecutionTimeout, setSdkRequestTimeout, withGeneralProgressListener, withRequestCredentialsProvider, withRequestMetricCollector, withSdkClientExecutionTimeout, withSdkRequestTimeout

  • Methods inherited from class java.lang.Object

    getClass, notify, notifyAll, wait, wait, wait

• Constructor Detail

  • CreateAutoMLJobRequest

    public CreateAutoMLJobRequest()

• Method Detail

  • setAutoMLJobName

    public void setAutoMLJobName(String autoMLJobName)

    Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

    Parameters:

    autoMLJobName - Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

  • getAutoMLJobName

    public String getAutoMLJobName()

    Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

    Returns:

    Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

  • withAutoMLJobName

    public CreateAutoMLJobRequest withAutoMLJobName(String autoMLJobName)

    Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

    Parameters:

    autoMLJobName - Identifies an Autopilot job. The name must be unique to your account and is case insensitive.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • getInputDataConfig

    public List<AutoMLChannel> getInputDataConfig()

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

    Returns:

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

  • setInputDataConfig

    public void setInputDataConfig(Collection<AutoMLChannel> inputDataConfig)

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

    Parameters:

    inputDataConfig - An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

  • withInputDataConfig

    public CreateAutoMLJobRequest withInputDataConfig(AutoMLChannel... inputDataConfig)

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

    NOTE: This method appends the values to the existing list (if any). Use setInputDataConfig(java.util.Collection) or withInputDataConfig(java.util.Collection) if you want to override the existing values.

    Parameters:

    inputDataConfig - An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • withInputDataConfig

    public CreateAutoMLJobRequest withInputDataConfig(Collection<AutoMLChannel> inputDataConfig)

    An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

    Parameters:

    inputDataConfig - An array of channel objects that describes the input data and its location. Each channel is a named input source. Similar to InputDataConfig supported by HyperParameterTrainingJobDefinition. Format(s) supported: CSV, Parquet. A minimum of 500 rows is required for the training dataset. There is not a minimum number of rows required for the validation dataset.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • setOutputDataConfig

    public void setOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

    Parameters:

    outputDataConfig - Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

  • getOutputDataConfig

    public AutoMLOutputDataConfig getOutputDataConfig()

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

    Returns:

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

  • withOutputDataConfig

    public CreateAutoMLJobRequest withOutputDataConfig(AutoMLOutputDataConfig outputDataConfig)

    Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

    Parameters:

    outputDataConfig - Provides information about encryption and the Amazon S3 output path needed to store artifacts from an AutoML job. Format(s) supported: CSV.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • setProblemType

    public void setProblemType(String problemType)

    Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    Parameters:

    problemType - Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    See Also:

    ProblemType

  • getProblemType

    public String getProblemType()

    Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    Returns:

    Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    See Also:

    ProblemType

  • withProblemType

    public CreateAutoMLJobRequest withProblemType(String problemType)

    Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    Parameters:

    problemType - Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

    See Also:

    ProblemType

  • withProblemType

    public CreateAutoMLJobRequest withProblemType(ProblemType problemType)

    Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    Parameters:

    problemType - Defines the type of supervised learning problem available for the candidates. For more information, see SageMaker Autopilot problem types.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

    See Also:

    ProblemType

  • setAutoMLJobObjective

    public void setAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective)

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

    Parameters:

    autoMLJobObjective - Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

  • getAutoMLJobObjective

    public AutoMLJobObjective getAutoMLJobObjective()

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

    Returns:

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

  • withAutoMLJobObjective

    public CreateAutoMLJobRequest withAutoMLJobObjective(AutoMLJobObjective autoMLJobObjective)

    Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

    Parameters:

    autoMLJobObjective - Specifies a metric to minimize or maximize as the objective of a job. If not specified, the default objective metric depends on the problem type. See AutoMLJobObjective for the default values.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • setAutoMLJobConfig

    public void setAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig)

    A collection of settings used to configure an AutoML job.

    Parameters:

    autoMLJobConfig - A collection of settings used to configure an AutoML job.

  • getAutoMLJobConfig

    public AutoMLJobConfig getAutoMLJobConfig()

    A collection of settings used to configure an AutoML job.

    Returns:

    A collection of settings used to configure an AutoML job.

  • withAutoMLJobConfig

    public CreateAutoMLJobRequest withAutoMLJobConfig(AutoMLJobConfig autoMLJobConfig)

    A collection of settings used to configure an AutoML job.

    Parameters:

    autoMLJobConfig - A collection of settings used to configure an AutoML job.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • setRoleArn

    public void setRoleArn(String roleArn)

    The ARN of the role that is used to access the data.

    Parameters:

    roleArn - The ARN of the role that is used to access the data.

  • getRoleArn

    public String getRoleArn()

    The ARN of the role that is used to access the data.

    Returns:

    The ARN of the role that is used to access the data.

  • withRoleArn

    public CreateAutoMLJobRequest withRoleArn(String roleArn)

    The ARN of the role that is used to access the data.

    Parameters:

    roleArn - The ARN of the role that is used to access the data.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • setGenerateCandidateDefinitionsOnly

    public void setGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly)

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

    Parameters:

    generateCandidateDefinitionsOnly - Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

  • getGenerateCandidateDefinitionsOnly

    public Boolean getGenerateCandidateDefinitionsOnly()

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

    Returns:

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

  • withGenerateCandidateDefinitionsOnly

    public CreateAutoMLJobRequest withGenerateCandidateDefinitionsOnly(Boolean generateCandidateDefinitionsOnly)

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

    Parameters:

    generateCandidateDefinitionsOnly - Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • isGenerateCandidateDefinitionsOnly

    public Boolean isGenerateCandidateDefinitionsOnly()

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

    Returns:

    Generates possible candidates without training the models. A candidate is a combination of data preprocessors, algorithms, and algorithm parameter settings.

  • getTags

    public List<Tag> getTags()

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    Returns:

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

  • setTags

    public void setTags(Collection<Tag> tags)

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    Parameters:

    tags - An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

  • withTags

    public CreateAutoMLJobRequest withTags(Tag... tags)

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    NOTE: This method appends the values to the existing list (if any). Use setTags(java.util.Collection) or withTags(java.util.Collection) if you want to override the existing values.

    Parameters:

    tags - An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • withTags

    public CreateAutoMLJobRequest withTags(Collection<Tag> tags)

    An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    Parameters:

    tags - An array of key-value pairs. You can use tags to categorize your Amazon Web Services resources in different ways, for example, by purpose, owner, or environment. For more information, see Tagging Amazon Web ServicesResources. Tag keys must be unique per resource.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • setModelDeployConfig

    public void setModelDeployConfig(ModelDeployConfig modelDeployConfig)

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

    Parameters:

    modelDeployConfig - Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

  • getModelDeployConfig

    public ModelDeployConfig getModelDeployConfig()

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

    Returns:

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

  • withModelDeployConfig

    public CreateAutoMLJobRequest withModelDeployConfig(ModelDeployConfig modelDeployConfig)

    Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

    Parameters:

    modelDeployConfig - Specifies how to generate the endpoint name for an automatic one-click Autopilot model deployment.

    Returns:

    Returns a reference to this object so that method calls can be chained together.

  • toString

    public 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

    Returns:

    A string representation of this object.

    See Also:

    Object.toString()

  • equals

    public boolean equals(Object obj)

    Overrides:

    equals in class Object

  • hashCode

    public int hashCode()

    Overrides:

    hashCode in class Object

  • clone

    public CreateAutoMLJobRequest clone()

    Description copied from class: AmazonWebServiceRequest
    Creates a shallow clone of this object for all fields except the handler context. Explicitly does not clone the deep structure of the other fields in the message.

    Overrides:

    clone in class AmazonWebServiceRequest

    See Also:

    Object.clone()