CreateMLModelRequest (AWS SDK For Java for OSGi 1.10.2 API)

java.lang.Object
- com.amazonaws.AmazonWebServiceRequest
- - com.amazonaws.services.machinelearning.model.CreateMLModelRequest

All Implemented Interfaces:

ReadLimitInfo, Serializable, Cloneable
```
public class CreateMLModelRequest
extends AmazonWebServiceRequest
implements Serializable, Cloneable
```
Container for the parameters to the CreateMLModel operation.
Creates a new MLModel using the data files and the recipe as information sources.

An MLModel is nearly immutable. Users can only update the MLModelName and the ScoreThreshold in an MLModel without creating a new MLModel .

CreateMLModel is an asynchronous operation. In response to CreateMLModel , Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel status to PENDING . After the MLModel is created and ready for use, Amazon ML sets the status to COMPLETED .

You can use the GetMLModel operation to check progress of the MLModel during the creation operation.

CreateMLModel requires a DataSource with computed statistics, which can be created by setting ComputeStatistics to true in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.

See Also:
AmazonMachineLearning.createMLModel(CreateMLModelRequest), Serialized Form

Field Summary
- Fields inherited from class com.amazonaws.AmazonWebServiceRequest
  NOOP

Constructor Summary

Constructors
Constructor and Description

CreateMLModelRequest()

Constructors
Constructor and Description
`CreateMLModelRequest()`

Method Summary

Methods
Modifier and Type	Method and Description
`CreateMLModelRequest`	`addParametersEntry(String key, String value)` A list of the training parameters in the `MLModel`.
`CreateMLModelRequest`	`clearParametersEntries()` Removes all the entries added into Parameters.
`CreateMLModelRequest`	`clone()` Creates a shallow clone of this request.
`boolean`	`equals(Object obj)`
`String`	`getMLModelId()` A user-supplied ID that uniquely identifies the `MLModel`.
`String`	`getMLModelName()` A user-supplied name or description of the `MLModel`.
`String`	`getMLModelType()` The category of supervised learning that this `MLModel` will address.
`Map<String,String>`	`getParameters()` A list of the training parameters in the `MLModel`.
`String`	`getRecipe()` The data recipe for creating `MLModel`.
`String`	`getRecipeUri()` The Amazon Simple Storage Service (Amazon S3) location and file name that contains the `MLModel` recipe.
`String`	`getTrainingDataSourceId()` The `DataSource` that points to the training data.
`int`	`hashCode()`
`void`	`setMLModelId(String mLModelId)` A user-supplied ID that uniquely identifies the `MLModel`.
`void`	`setMLModelName(String mLModelName)` A user-supplied name or description of the `MLModel`.
`void`	`setMLModelType(MLModelType mLModelType)` The category of supervised learning that this `MLModel` will address.
`void`	`setMLModelType(String mLModelType)` The category of supervised learning that this `MLModel` will address.
`void`	`setParameters(Map<String,String> parameters)` A list of the training parameters in the `MLModel`.
`void`	`setRecipe(String recipe)` The data recipe for creating `MLModel`.
`void`	`setRecipeUri(String recipeUri)` The Amazon Simple Storage Service (Amazon S3) location and file name that contains the `MLModel` recipe.
`void`	`setTrainingDataSourceId(String trainingDataSourceId)` The `DataSource` that points to the training data.
`String`	`toString()` Returns a string representation of this object; useful for testing and debugging.
`CreateMLModelRequest`	`withMLModelId(String mLModelId)` A user-supplied ID that uniquely identifies the `MLModel`.
`CreateMLModelRequest`	`withMLModelName(String mLModelName)` A user-supplied name or description of the `MLModel`.
`CreateMLModelRequest`	`withMLModelType(MLModelType mLModelType)` The category of supervised learning that this `MLModel` will address.
`CreateMLModelRequest`	`withMLModelType(String mLModelType)` The category of supervised learning that this `MLModel` will address.
`CreateMLModelRequest`	`withParameters(Map<String,String> parameters)` A list of the training parameters in the `MLModel`.
`CreateMLModelRequest`	`withRecipe(String recipe)` The data recipe for creating `MLModel`.
`CreateMLModelRequest`	`withRecipeUri(String recipeUri)` The Amazon Simple Storage Service (Amazon S3) location and file name that contains the `MLModel` recipe.
`CreateMLModelRequest`	`withTrainingDataSourceId(String trainingDataSourceId)` The `DataSource` that points to the training data.

Methods inherited from class com.amazonaws.AmazonWebServiceRequest
copyBaseTo, getCustomRequestHeaders, getGeneralProgressListener, getReadLimit, getRequestClientOptions, getRequestCredentials, getRequestMetricCollector, putCustomRequestHeader, setGeneralProgressListener, setRequestCredentials, setRequestMetricCollector, withGeneralProgressListener, withRequestMetricCollector

Methods inherited from class java.lang.Object
finalize, getClass, notify, notifyAll, wait, wait, wait

- Constructor Detail
  - CreateMLModelRequest
```
public CreateMLModelRequest()
```
- Method Detail
  - getMLModelId
```
public String getMLModelId()
```
    A user-supplied ID that uniquely identifies the MLModel.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Returns:
    A user-supplied ID that uniquely identifies the MLModel.
  - setMLModelId
```
public void setMLModelId(String mLModelId)
```
    A user-supplied ID that uniquely identifies the MLModel.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    mLModelId - A user-supplied ID that uniquely identifies the MLModel.
  - withMLModelId
```
public CreateMLModelRequest withMLModelId(String mLModelId)
```
    A user-supplied ID that uniquely identifies the MLModel.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    mLModelId - A user-supplied ID that uniquely identifies the MLModel.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - getMLModelName
```
public String getMLModelName()
```
    A user-supplied name or description of the MLModel.
    Constraints:
    Length: 0 - 1024
    Pattern: .*\S.*|^$
    
    Returns:
    A user-supplied name or description of the MLModel.
  - setMLModelName
```
public void setMLModelName(String mLModelName)
```
    A user-supplied name or description of the MLModel.
    Constraints:
    Length: 0 - 1024
    Pattern: .*\S.*|^$
    
    Parameters:
    mLModelName - A user-supplied name or description of the MLModel.
  - withMLModelName
```
public CreateMLModelRequest withMLModelName(String mLModelName)
```
    A user-supplied name or description of the MLModel.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 0 - 1024
    Pattern: .*\S.*|^$
    
    Parameters:
    mLModelName - A user-supplied name or description of the MLModel.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - getMLModelType
```
public String getMLModelType()
```
    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.
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    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:
    MLModelType
  - setMLModelType
```
public void setMLModelType(String 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.
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    Parameters:
    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.
    See Also:
    MLModelType
  - withMLModelType
```
public CreateMLModelRequest withMLModelType(String 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.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    Parameters:
    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.
    Returns:
    A reference to this updated object so that method calls can be chained together.
    See Also:
    MLModelType
  - setMLModelType
```
public void setMLModelType(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.
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    Parameters:
    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.
    See Also:
    MLModelType
  - withMLModelType
```
public CreateMLModelRequest withMLModelType(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.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Allowed Values: REGRESSION, BINARY, MULTICLASS
    Parameters:
    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.
    Returns:
    A reference to this updated object so that method calls can be chained together.
    See Also:
    MLModelType
  - getParameters
```
public Map<String,String> getParameters()
```
    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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    - sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.
    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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    
    sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.
  - setParameters
```
public void setParameters(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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    - sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.
    Parameters:
    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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    
    sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.
  - withParameters
```
public CreateMLModelRequest withParameters(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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    - sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.
    Returns a reference to this object so that method calls can be chained together.
    Parameters:
    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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    
    sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    
    sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - addParametersEntry
```
public CreateMLModelRequest addParametersEntry(String key,
                                      String value)
```
    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.l1RegularizationAmount - Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in 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 not to use L1 normalization. The parameter cannot be used when L2 is specified. Use this parameter sparingly.
    - sgd.l2RegularizationAmount - 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1 is specified. Use this parameter sparingly.
    - sgd.maxPasses - 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.maxMLModelSizeInBytes - 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.
    The method adds a new key-value pair into Parameters parameter, and returns a reference to this object so that method calls can be chained together.
    Parameters:
    key - The key of the entry to be added into Parameters.
    value - The corresponding value of the entry to be added into Parameters.
  - clearParametersEntries
```
public CreateMLModelRequest clearParametersEntries()
```
    Removes all the entries added into Parameters.
    Returns a reference to this object so that method calls can be chained together.
  - getTrainingDataSourceId
```
public String getTrainingDataSourceId()
```
    The DataSource that points to the training data.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Returns:
    The DataSource that points to the training data.
  - setTrainingDataSourceId
```
public void setTrainingDataSourceId(String trainingDataSourceId)
```
    The DataSource that points to the training data.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    trainingDataSourceId - The DataSource that points to the training data.
  - withTrainingDataSourceId
```
public CreateMLModelRequest withTrainingDataSourceId(String trainingDataSourceId)
```
    The DataSource that points to the training data.
    Returns a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 1 - 64
    Pattern: [a-zA-Z0-9_.-]+
    
    Parameters:
    trainingDataSourceId - The DataSource that points to the training data.
    
    Returns:
    A reference to this updated object so that method calls can be chained together.
  - getRecipe
```
public String getRecipe()
```
    The data recipe for creating 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.
    Constraints:
    Length: 0 - 131071
    
    Returns:
    The data recipe for creating 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.
  - setRecipe
```
public void setRecipe(String recipe)
```
    The data recipe for creating 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.
    Constraints:
    Length: 0 - 131071
    
    Parameters:
    recipe - The data recipe for creating 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.
  - withRecipe
```
public CreateMLModelRequest withRecipe(String recipe)
```
    The data recipe for creating 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 a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 0 - 131071
    
    Parameters:
    recipe - The data recipe for creating 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:
    A reference to this updated object so that method calls can be chained together.
  - getRecipeUri
```
public String getRecipeUri()
```
    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.
    Constraints:
    Length: 0 - 2048
    Pattern: s3://([^/]+)(/.*)?
    
    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.
  - setRecipeUri
```
public void setRecipeUri(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.
    Constraints:
    Length: 0 - 2048
    Pattern: s3://([^/]+)(/.*)?
    
    Parameters:
    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.
  - withRecipeUri
```
public CreateMLModelRequest withRecipeUri(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 a reference to this object so that method calls can be chained together.
    Constraints:
    Length: 0 - 2048
    Pattern: s3://([^/]+)(/.*)?
    
    Parameters:
    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:
    A reference to this updated object so that method calls can be chained together.
  - toString
```
public String toString()
```
    Returns a string representation of this object; useful for testing and debugging.
    
    Overrides:
    
    toString in class Object
    
    Returns:
    A string representation of this object.
    See Also:
    Object.toString()
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - clone
```
public CreateMLModelRequest clone()
```
    Description copied from class: AmazonWebServiceRequest
    
    Creates a shallow clone of this request. Explicitly does not clone the deep structure of the request object.
    
    Overrides:
    
    clone in class AmazonWebServiceRequest
    
    See Also:
    Object.clone()

Class CreateMLModelRequest

Field Summary

Fields inherited from class com.amazonaws.AmazonWebServiceRequest

Constructor Summary

Method Summary

Methods inherited from class com.amazonaws.AmazonWebServiceRequest

Methods inherited from class java.lang.Object

Constructor Detail

CreateMLModelRequest

Method Detail

getMLModelId

setMLModelId

withMLModelId

getMLModelName

setMLModelName

withMLModelName

getMLModelType

setMLModelType

withMLModelType

setMLModelType

withMLModelType

getParameters

setParameters

withParameters

addParametersEntry

clearParametersEntries

getTrainingDataSourceId

setTrainingDataSourceId

withTrainingDataSourceId

getRecipe

setRecipe

withRecipe

getRecipeUri

setRecipeUri

withRecipeUri

toString

hashCode

equals

clone