MLModel (AWS SDK for Java - 2.0.0-preview-1)

java.lang.Object
- software.amazon.awssdk.services.machinelearning.model.MLModel

All Implemented Interfaces:

StructuredPojo, ToCopyableBuilder<MLModel.Builder,MLModel>
```
@Generated("software.amazon.awssdk:codegen")
public class MLModel
extends Object
implements StructuredPojo, ToCopyableBuilder<MLModel.Builder,MLModel>
```
Represents the output of a GetMLModel operation.

The content consists of the detailed metadata and the current status of the MLModel.

Nested Class Summary

Nested Classes
Modifier and Type Class Description

static interface MLModel.Builder

Nested Classes
Modifier and Type	Class	Description
`static interface`	`MLModel.Builder`

Method Summary

All Methods Static Methods Instance Methods Concrete Methods
Modifier and Type	Method	Description
`String`	`algorithm()`	The algorithm used to train the `MLModel`.
`static MLModel.Builder`	`builder()`
`Long`	`computeTime()`
`Date`	`createdAt()`	The time that the `MLModel` was created.
`String`	`createdByIamUser()`	The AWS user account from which the `MLModel` was created.
`RealtimeEndpointInfo`	`endpointInfo()`	The current endpoint of the `MLModel`.
`boolean`	`equals(Object obj)`
`Date`	`finishedAt()`
`int`	`hashCode()`
`String`	`inputDataLocationS3()`	The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
`Date`	`lastUpdatedAt()`	The time of the most recent edit to the `MLModel`.
`void`	`marshall(ProtocolMarshaller protocolMarshaller)`	Marshalls this structured data using the given `ProtocolMarshaller`.
`String`	`message()`	A description of the most recent details about accessing the `MLModel`.
`String`	`mlModelId()`	The ID assigned to the `MLModel` at creation.
`String`	`mlModelType()`	Identifies the `MLModel` category.
`String`	`name()`	A user-supplied name or description of the `MLModel`.
`Float`	`scoreThreshold()`
`Date`	`scoreThresholdLastUpdatedAt()`	The time of the most recent edit to the `ScoreThreshold`.
`static Class<? extends MLModel.Builder>`	`serializableBuilderClass()`
`Long`	`sizeInBytes()`
`Date`	`startedAt()`
`String`	`status()`	The current status of an `MLModel`.
`MLModel.Builder`	`toBuilder()`	Take this object and create a builder that contains all of the current property values of this object.
`String`	`toString()`
`String`	`trainingDataSourceId()`	The ID of the training `DataSource`.
`Map<String,String>`	`trainingParameters()`	A list of the training parameters in the `MLModel`.

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

- Method Detail
  - mlModelId
```
public String mlModelId()
```
    The ID assigned to the MLModel at creation.
    
    Returns:
    
    The ID assigned to the MLModel at creation.
  - trainingDataSourceId
```
public String trainingDataSourceId()
```
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
    
    Returns:
    
    The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
  - createdByIamUser
```
public 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.
    
    Returns:
    
    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.
  - createdAt
```
public Date createdAt()
```
    The time that the MLModel was created. The time is expressed in epoch time.
    
    Returns:
    
    The time that the MLModel was created. The time is expressed in epoch time.
  - lastUpdatedAt
```
public Date lastUpdatedAt()
```
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
    
    Returns:
    
    The time of the most recent edit to the MLModel. The time is expressed in epoch time.
  - name
```
public String name()
```
    A user-supplied name or description of the MLModel.
    
    Returns:
    
    A user-supplied name or description of the MLModel.
  - status
```
public 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.
    Returns:
    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.
    See Also:
    
    EntityStatus
  - sizeInBytes
```
public Long sizeInBytes()
```
    Returns:
  - endpointInfo
```
public RealtimeEndpointInfo endpointInfo()
```
    The current endpoint of the MLModel.
    
    Returns:
    
    The current endpoint of the MLModel.
  - trainingParameters
```
public 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.
    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.
    
    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.
  - inputDataLocationS3
```
public String inputDataLocationS3()
```
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
    
    Returns:
    
    The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
  - algorithm
```
public 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.
    Returns:
    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.
    See Also:
    
    Algorithm
  - mlModelType
```
public 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?".
    Returns:
    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?".
    See Also:
    
    MLModelType
  - scoreThreshold
```
public Float scoreThreshold()
```
    Returns:
  - scoreThresholdLastUpdatedAt
```
public Date scoreThresholdLastUpdatedAt()
```
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
    
    Returns:
    
    The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
  - message
```
public String message()
```
    A description of the most recent details about accessing the MLModel.
    
    Returns:
    
    A description of the most recent details about accessing the MLModel.
  - computeTime
```
public Long computeTime()
```
    Returns:
  - finishedAt
```
public Date finishedAt()
```
    Returns:
  - startedAt
```
public Date startedAt()
```
    Returns:
  - toBuilder
```
public MLModel.Builder toBuilder()
```
    Description copied from interface: ToCopyableBuilder
    
    Take this object and create a builder that contains all of the current property values of this object.
    
    Specified by:
    
    toBuilder in interface ToCopyableBuilder<MLModel.Builder,MLModel>
    
    Returns:
    
    a builder for type T
  - builder
```
public static MLModel.Builder builder()
```
  - serializableBuilderClass
```
public static Class<? extends MLModel.Builder> serializableBuilderClass()
```
  - hashCode
```
public int hashCode()
```
    Overrides:
    
    hashCode in class Object
  - equals
```
public boolean equals(Object obj)
```
    Overrides:
    
    equals in class Object
  - toString
```
public String toString()
```
    Overrides:
    
    toString in class Object
  - marshall
```
public void marshall(ProtocolMarshaller protocolMarshaller)
```
    Description copied from interface: StructuredPojo
    
    Marshalls this structured data using the given ProtocolMarshaller.
    
    Specified by:
    
    marshall in interface StructuredPojo
    
    Parameters:
    
    protocolMarshaller - Implementation of ProtocolMarshaller used to marshall this object's data.

Class MLModel

Nested Class Summary

Method Summary

Methods inherited from class java.lang.Object

Method Detail

mlModelId

trainingDataSourceId

createdByIamUser

createdAt

lastUpdatedAt

name

status

sizeInBytes

endpointInfo

trainingParameters

inputDataLocationS3

algorithm

mlModelType

scoreThreshold

scoreThresholdLastUpdatedAt

message

computeTime

finishedAt

startedAt

toBuilder

builder

serializableBuilderClass

hashCode

equals

toString

marshall