software.amazon.awssdk.services.machinelearning.model

Interface GetMlModelResponse.Builder

All Superinterfaces:

AwsResponse.Builder, Buildable, CopyableBuilder<GetMlModelResponse.Builder,GetMlModelResponse>, MachineLearningResponse.Builder, SdkBuilder<GetMlModelResponse.Builder,GetMlModelResponse>, SdkPojo, SdkResponse.Builder

Enclosing class:

GetMlModelResponse

public static interface GetMlModelResponse.Builder
extends MachineLearningResponse.Builder, SdkPojo, CopyableBuilder<GetMlModelResponse.Builder,GetMlModelResponse>

Method Summary

Modifier and Type	Method and Description
GetMlModelResponse.Builder	computeTime(Long computeTime) The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources.
GetMlModelResponse.Builder	createdAt(Instant createdAt) The time that the MLModel was created.
GetMlModelResponse.Builder	createdByIamUser(String createdByIamUser) The AWS user account from which the MLModel was created.
default GetMlModelResponse.Builder	endpointInfo(Consumer<RealtimeEndpointInfo.Builder> endpointInfo) The current endpoint of the MLModel
GetMlModelResponse.Builder	endpointInfo(RealtimeEndpointInfo endpointInfo) The current endpoint of the MLModel
GetMlModelResponse.Builder	finishedAt(Instant finishedAt) The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED.
GetMlModelResponse.Builder	inputDataLocationS3(String inputDataLocationS3) The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
GetMlModelResponse.Builder	lastUpdatedAt(Instant lastUpdatedAt) The time of the most recent edit to the MLModel.
GetMlModelResponse.Builder	logUri(String logUri) A link to the file that contains logs of the CreateMLModel operation.
GetMlModelResponse.Builder	message(String message) A description of the most recent details about accessing the MLModel.
GetMlModelResponse.Builder	mlModelId(String mlModelId) The MLModel ID, which is same as the MLModelId in the request.
GetMlModelResponse.Builder	mlModelType(MLModelType mlModelType) Identifies the MLModel category.
GetMlModelResponse.Builder	mlModelType(String mlModelType) Identifies the MLModel category.
GetMlModelResponse.Builder	name(String name) A user-supplied name or description of the MLModel.
GetMlModelResponse.Builder	recipe(String recipe) The recipe to use when training the MLModel.
GetMlModelResponse.Builder	schema(String schema) The schema used by all of the data files referenced by the DataSource.
GetMlModelResponse.Builder	scoreThreshold(Float scoreThreshold) The scoring threshold is used in binary classification MLModel models.
GetMlModelResponse.Builder	scoreThresholdLastUpdatedAt(Instant scoreThresholdLastUpdatedAt) The time of the most recent edit to the ScoreThreshold.
GetMlModelResponse.Builder	sizeInBytes(Long sizeInBytes) Sets the value of the SizeInBytes property for this object.
GetMlModelResponse.Builder	startedAt(Instant startedAt) The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS.
GetMlModelResponse.Builder	status(EntityStatus status) The current status of the MLModel.
GetMlModelResponse.Builder	status(String status) The current status of the MLModel.
GetMlModelResponse.Builder	trainingDataSourceId(String trainingDataSourceId) The ID of the training DataSource.
GetMlModelResponse.Builder	trainingParameters(Map<String,String> trainingParameters) A list of the training parameters in the MLModel.

Methods inherited from interface software.amazon.awssdk.services.machinelearning.model.MachineLearningResponse.Builder

build, responseMetadata, responseMetadata

Methods inherited from interface software.amazon.awssdk.core.SdkResponse.Builder

sdkHttpResponse, sdkHttpResponse

Methods inherited from interface software.amazon.awssdk.core.SdkPojo

sdkFields

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

copy

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

applyMutation, build

Method Detail

mlModelId

GetMlModelResponse.Builder mlModelId(String mlModelId)

The MLModel ID, which is same as the MLModelId in the request.

Parameters:

mlModelId - The MLModel ID, which is same as the MLModelId in the request.

Returns:

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

trainingDataSourceId

GetMlModelResponse.Builder trainingDataSourceId(String trainingDataSourceId)

The ID of the training DataSource.

Parameters:

trainingDataSourceId - The ID of the training DataSource.

Returns:

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

createdByIamUser

GetMlModelResponse.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

GetMlModelResponse.Builder createdAt(Instant 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

GetMlModelResponse.Builder lastUpdatedAt(Instant 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

GetMlModelResponse.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

GetMlModelResponse.Builder status(String status)

The current status of the MLModel. This element can have one of the following values:

• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
• INPROGRESS - The request is processing.
• FAILED - The request did not run to completion. The ML model isn't usable.
• COMPLETED - The request completed successfully.
• DELETED - The MLModel is marked as deleted. It isn't usable.

Parameters:

status - The current status of the MLModel. This element can have one of the following values:

• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
• INPROGRESS - The request is processing.
• FAILED - The request did not run to completion. The ML model isn't usable.
• COMPLETED - The request 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, EntityStatus

status

GetMlModelResponse.Builder status(EntityStatus status)

The current status of the MLModel. This element can have one of the following values:

• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
• INPROGRESS - The request is processing.
• FAILED - The request did not run to completion. The ML model isn't usable.
• COMPLETED - The request completed successfully.
• DELETED - The MLModel is marked as deleted. It isn't usable.

Parameters:

status - The current status of the MLModel. This element can have one of the following values:

• PENDING - Amazon Machine Learning (Amazon ML) submitted a request to describe a MLModel.
• INPROGRESS - The request is processing.
• FAILED - The request did not run to completion. The ML model isn't usable.
• COMPLETED - The request 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, EntityStatus

sizeInBytes

GetMlModelResponse.Builder sizeInBytes(Long sizeInBytes)

Sets the value of the SizeInBytes property for this object.

Parameters:

sizeInBytes - The new value for the SizeInBytes property for this object.

Returns:

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

endpointInfo

GetMlModelResponse.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.

endpointInfo

default GetMlModelResponse.Builder endpointInfo(Consumer<RealtimeEndpointInfo.Builder> endpointInfo)

The current endpoint of the MLModel

This is a convenience that creates an instance of the RealtimeEndpointInfo.Builder avoiding the need to create one manually via RealtimeEndpointInfo.builder(). When the Consumer completes, SdkBuilder.build() is called immediately and its result is passed to endpointInfo(RealtimeEndpointInfo).

Parameters:

endpointInfo - a consumer that will call methods on RealtimeEndpointInfo.Builder

Returns:

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

See Also:

endpointInfo(RealtimeEndpointInfo)

trainingParameters

GetMlModelResponse.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 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.

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 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.

Returns:

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

inputDataLocationS3

GetMlModelResponse.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.

mlModelType

GetMlModelResponse.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 an e-commerce website?"
• 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 an e-commerce website?"
• 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

mlModelType

GetMlModelResponse.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 an e-commerce website?"
• 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 an e-commerce website?"
• 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

scoreThreshold

GetMlModelResponse.Builder scoreThreshold(Float scoreThreshold)

The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

Parameters:

scoreThreshold - The scoring threshold is used in binary classification MLModel models. It marks the boundary between a positive prediction and a negative prediction.

Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.

Returns:

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

scoreThresholdLastUpdatedAt

GetMlModelResponse.Builder scoreThresholdLastUpdatedAt(Instant 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.

logUri

GetMlModelResponse.Builder logUri(String logUri)

A link to the file that contains logs of the CreateMLModel operation.

Parameters:

logUri - A link to the file that contains logs of the CreateMLModel operation.

Returns:

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

message

GetMlModelResponse.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

GetMlModelResponse.Builder computeTime(Long computeTime)

The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

Parameters:

computeTime - The approximate CPU time in milliseconds that Amazon Machine Learning spent processing the MLModel, normalized and scaled on computation resources. ComputeTime is only available if the MLModel is in the COMPLETED state.

Returns:

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

finishedAt

GetMlModelResponse.Builder finishedAt(Instant finishedAt)

The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

Parameters:

finishedAt - The epoch time when Amazon Machine Learning marked the MLModel as COMPLETED or FAILED. FinishedAt is only available when the MLModel is in the COMPLETED or FAILED state.

Returns:

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

startedAt

GetMlModelResponse.Builder startedAt(Instant startedAt)

The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS. StartedAt isn't available if the MLModel is in the PENDING state.

Parameters:

startedAt - The epoch time when Amazon Machine Learning marked the MLModel as INPROGRESS . StartedAt isn't available if the MLModel is in the PENDING state.

Returns:

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

recipe

GetMlModelResponse.Builder recipe(String recipe)

The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

This parameter is provided as part of the verbose format.

Parameters:

recipe - The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, and manipulations to perform on the observation data during training.

This parameter is provided as part of the verbose format.

Returns:

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

schema

GetMlModelResponse.Builder schema(String schema)

The schema used by all of the data files referenced by the DataSource.

This parameter is provided as part of the verbose format.

Parameters:

schema - The schema used by all of the data files referenced by the DataSource.

This parameter is provided as part of the verbose format.

Returns:

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