MLModel.Builder (AWS SDK for Java

All Superinterfaces:

CopyableBuilder<MLModel.Builder,MLModel>, SdkBuilder<MLModel.Builder,MLModel>

Enclosing class:

MLModel
```
public static interface MLModel.Builder
extends CopyableBuilder<MLModel.Builder,MLModel>
```

Method Summary

All Methods Instance Methods Abstract Methods
Modifier and Type	Method	Description
`MLModel.Builder`	`algorithm(String algorithm)`	The algorithm used to train the `MLModel`.
`MLModel.Builder`	`algorithm(Algorithm algorithm)`	The algorithm used to train the `MLModel`.
`MLModel.Builder`	`computeTime(Long computeTime)`
`MLModel.Builder`	`createdAt(Date createdAt)`	The time that the `MLModel` was created.
`MLModel.Builder`	`createdByIamUser(String createdByIamUser)`	The AWS user account from which the `MLModel` was created.
`MLModel.Builder`	`endpointInfo(RealtimeEndpointInfo endpointInfo)`	The current endpoint of the `MLModel`.
`MLModel.Builder`	`finishedAt(Date finishedAt)`
`MLModel.Builder`	`inputDataLocationS3(String inputDataLocationS3)`	The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
`MLModel.Builder`	`lastUpdatedAt(Date lastUpdatedAt)`	The time of the most recent edit to the `MLModel`.
`MLModel.Builder`	`message(String message)`	A description of the most recent details about accessing the `MLModel`.
`MLModel.Builder`	`mlModelId(String mlModelId)`	The ID assigned to the `MLModel` at creation.
`MLModel.Builder`	`mlModelType(String mlModelType)`	Identifies the `MLModel` category.
`MLModel.Builder`	`mlModelType(MLModelType mlModelType)`	Identifies the `MLModel` category.
`MLModel.Builder`	`name(String name)`	A user-supplied name or description of the `MLModel`.
`MLModel.Builder`	`scoreThreshold(Float scoreThreshold)`
`MLModel.Builder`	`scoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)`	The time of the most recent edit to the `ScoreThreshold`.
`MLModel.Builder`	`sizeInBytes(Long sizeInBytes)`
`MLModel.Builder`	`startedAt(Date startedAt)`
`MLModel.Builder`	`status(String status)`	The current status of an `MLModel`.
`MLModel.Builder`	`status(EntityStatus status)`	The current status of an `MLModel`.
`MLModel.Builder`	`trainingDataSourceId(String trainingDataSourceId)`	The ID of the training `DataSource`.
`MLModel.Builder`	`trainingParameters(Map<String,String> trainingParameters)`	A list of the training parameters in the `MLModel`.

Methods inherited from interface software.amazon.awssdk.utils.builder.CopyableBuilder
copy

Methods inherited from interface software.amazon.awssdk.utils.builder.SdkBuilder
apply, build

- Method Detail
  - mlModelId
```
MLModel.Builder mlModelId(String mlModelId)
```
    The ID assigned to the MLModel at creation.
    
    Parameters:
    
    mlModelId - The ID assigned to the MLModel at creation.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - trainingDataSourceId
```
MLModel.Builder trainingDataSourceId(String trainingDataSourceId)
```
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    
    Parameters:
    
    trainingDataSourceId - The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - createdByIamUser
```
MLModel.Builder createdByIamUser(String createdByIamUser)
```
    The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    
    Parameters:
    
    createdByIamUser - The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - createdAt
```
MLModel.Builder createdAt(Date createdAt)
```
    The time that the MLModel was created. The time is expressed in epoch time.
    
    Parameters:
    
    createdAt - The time that the MLModel was created. The time is expressed in epoch time.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - lastUpdatedAt
```
MLModel.Builder lastUpdatedAt(Date lastUpdatedAt)
```
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    
    Parameters:
    
    lastUpdatedAt - The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - name
```
MLModel.Builder name(String name)
```
    A user-supplied name or description of the MLModel.
    
    Parameters:
    
    name - A user-supplied name or description of the MLModel.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - status
```
MLModel.Builder status(String status)
```
    The current status of an MLModel. This element can have one of the following values:
    - PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    - INPROGRESS - The creation process is underway.
    - FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    Parameters:
    status - The current status of an MLModel. This element can have one of the following values:
    
    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    
    INPROGRESS - The creation process is underway.
    
    FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't usable.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    EntityStatus
  - status
```
MLModel.Builder status(EntityStatus status)
```
    The current status of an MLModel. This element can have one of the following values:
    - PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    - INPROGRESS - The creation process is underway.
    - FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.
    - COMPLETED - The creation process completed successfully.
    - DELETED - The MLModel is marked as deleted. It isn't usable.
    Parameters:
    status - The current status of an MLModel. This element can have one of the following values:
    
    PENDING - Amazon Machine Learning (Amazon ML) submitted a request to create an MLModel.
    
    INPROGRESS - The creation process is underway.
    
    FAILED - The request to create an MLModel didn't run to completion. The model isn't usable.
    
    COMPLETED - The creation process completed successfully.
    
    DELETED - The MLModel is marked as deleted. It isn't usable.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    EntityStatus
  - sizeInBytes
```
MLModel.Builder sizeInBytes(Long sizeInBytes)
```
    Parameters:
    
    sizeInBytes -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - endpointInfo
```
MLModel.Builder endpointInfo(RealtimeEndpointInfo endpointInfo)
```
    The current endpoint of the MLModel.
    
    Parameters:
    
    endpointInfo - The current endpoint of the MLModel.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - trainingParameters
```
MLModel.Builder trainingParameters(Map<String,String> trainingParameters)
```
    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.
    - sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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.
    Parameters:
    trainingParameters - 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.
    
    sgd.l1RegularizationAmount - The coefficient regularization L1 norm, which controls overfitting the data by penalizing large coefficients. This parameter 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 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, which 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.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - inputDataLocationS3
```
MLModel.Builder inputDataLocationS3(String inputDataLocationS3)
```
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    
    Parameters:
    
    inputDataLocationS3 - The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - algorithm
```
MLModel.Builder algorithm(String algorithm)
```
    The algorithm used to train the MLModel. The following algorithm is supported:
    - SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Parameters:
    algorithm - The algorithm used to train the MLModel. The following algorithm is supported:
    
    SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    Algorithm
  - algorithm
```
MLModel.Builder algorithm(Algorithm algorithm)
```
    The algorithm used to train the MLModel. The following algorithm is supported:
    - SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Parameters:
    algorithm - The algorithm used to train the MLModel. The following algorithm is supported:
    
    SGD -- Stochastic gradient descent. The goal of SGD is to minimize the gradient of the loss function.
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    Algorithm
  - mlModelType
```
MLModel.Builder mlModelType(String mlModelType)
```
    Identifies the MLModel category. The following are the available types:
    - REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"
    - BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    - MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
    Parameters:
    mlModelType - Identifies the MLModel category. The following are the available types:
    
    REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    MLModelType
  - mlModelType
```
MLModel.Builder mlModelType(MLModelType mlModelType)
```
    Identifies the MLModel category. The following are the available types:
    - REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"
    - BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    - MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
    Parameters:
    mlModelType - Identifies the MLModel category. The following are the available types:
    
    REGRESSION - Produces a numeric result. For example, "What price should a house be listed at?"
    
    BINARY - Produces one of two possible results. For example, "Is this a child-friendly web site?".
    
    MULTICLASS - Produces one of several possible results. For example, "Is this a HIGH-, LOW-, or MEDIUM-risk trade?".
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
    
    See Also:
    
    MLModelType
  - scoreThreshold
```
MLModel.Builder scoreThreshold(Float scoreThreshold)
```
    Parameters:
    
    scoreThreshold -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - scoreThresholdLastUpdatedAt
```
MLModel.Builder scoreThresholdLastUpdatedAt(Date scoreThresholdLastUpdatedAt)
```
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Parameters:
    
    scoreThresholdLastUpdatedAt - The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - message
```
MLModel.Builder message(String message)
```
    A description of the most recent details about accessing the MLModel.
    
    Parameters:
    
    message - A description of the most recent details about accessing the MLModel.
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - computeTime
```
MLModel.Builder computeTime(Long computeTime)
```
    Parameters:
    
    computeTime -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - finishedAt
```
MLModel.Builder finishedAt(Date finishedAt)
```
    Parameters:
    
    finishedAt -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.
  - startedAt
```
MLModel.Builder startedAt(Date startedAt)
```
    Parameters:
    
    startedAt -
    
    Returns:
    
    Returns a reference to this object so that method calls can be chained together.

Interface MLModel.Builder

Method Summary

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

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

Method Detail

mlModelId

trainingDataSourceId

createdByIamUser

createdAt

lastUpdatedAt

name

status

status

sizeInBytes

endpointInfo

trainingParameters

inputDataLocationS3

algorithm

algorithm

mlModelType

mlModelType

scoreThreshold

scoreThresholdLastUpdatedAt

message

computeTime

finishedAt

startedAt