public static interface MLModel.Builder extends CopyableBuilder<MLModel.Builder,MLModel>
Modifier and Type | Method and Description |
---|---|
MLModel.Builder |
algorithm(Algorithm algorithm)
The algorithm used to train the
MLModel . |
MLModel.Builder |
algorithm(String algorithm)
The algorithm used to train the
MLModel . |
MLModel.Builder |
computeTime(Long computeTime)
Sets the value of the ComputeTime property for this object.
|
MLModel.Builder |
createdAt(Instant createdAt)
The time that the
MLModel was created. |
MLModel.Builder |
createdByIamUser(String createdByIamUser)
The AWS user account from which the
MLModel was created. |
default MLModel.Builder |
endpointInfo(Consumer<RealtimeEndpointInfo.Builder> endpointInfo)
The current endpoint of the
MLModel . |
MLModel.Builder |
endpointInfo(RealtimeEndpointInfo endpointInfo)
The current endpoint of the
MLModel . |
MLModel.Builder |
finishedAt(Instant finishedAt)
Sets the value of the FinishedAt property for this object.
|
MLModel.Builder |
inputDataLocationS3(String inputDataLocationS3)
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
|
MLModel.Builder |
lastUpdatedAt(Instant 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(MLModelType mlModelType)
Identifies the
MLModel category. |
MLModel.Builder |
mlModelType(String mlModelType)
Identifies the
MLModel category. |
MLModel.Builder |
name(String name)
A user-supplied name or description of the
MLModel . |
MLModel.Builder |
scoreThreshold(Float scoreThreshold)
Sets the value of the ScoreThreshold property for this object.
|
MLModel.Builder |
scoreThresholdLastUpdatedAt(Instant scoreThresholdLastUpdatedAt)
The time of the most recent edit to the
ScoreThreshold . |
MLModel.Builder |
sizeInBytes(Long sizeInBytes)
Sets the value of the SizeInBytes property for this object.
|
MLModel.Builder |
startedAt(Instant startedAt)
Sets the value of the StartedAt property for this object.
|
MLModel.Builder |
status(EntityStatus status)
The current status of an
MLModel . |
MLModel.Builder |
status(String 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 . |
copy
apply, build
MLModel.Builder mlModelId(String mlModelId)
The ID assigned to the MLModel
at creation.
mlModelId
- The ID assigned to the MLModel
at creation.MLModel.Builder trainingDataSourceId(String trainingDataSourceId)
The ID of the training DataSource
. The CreateMLModel
operation uses the
TrainingDataSourceId
.
trainingDataSourceId
- The ID of the training DataSource
. The CreateMLModel
operation uses the
TrainingDataSourceId
.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.
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.MLModel.Builder createdAt(Instant createdAt)
The time that the MLModel
was created. The time is expressed in epoch time.
createdAt
- The time that the MLModel
was created. The time is expressed in epoch time.MLModel.Builder lastUpdatedAt(Instant lastUpdatedAt)
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
lastUpdatedAt
- The time of the most recent edit to the MLModel
. The time is expressed in epoch time.MLModel.Builder name(String name)
A user-supplied name or description of the MLModel
.
name
- A user-supplied name or description of the MLModel
.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.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.EntityStatus
,
EntityStatus
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.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.EntityStatus
,
EntityStatus
MLModel.Builder sizeInBytes(Long sizeInBytes)
sizeInBytes
- The new value for the SizeInBytes property for this object.MLModel.Builder endpointInfo(RealtimeEndpointInfo endpointInfo)
The current endpoint of the MLModel
.
endpointInfo
- The current endpoint of the MLModel
.default MLModel.Builder endpointInfo(Consumer<RealtimeEndpointInfo.Builder> endpointInfo)
The current endpoint of the MLModel
.
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)
.endpointInfo
- a consumer that will call methods on RealtimeEndpointInfo.Builder
endpointInfo(RealtimeEndpointInfo)
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.
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.
MLModel.Builder inputDataLocationS3(String inputDataLocationS3)
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
inputDataLocationS3
- The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).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.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.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.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.Algorithm
,
Algorithm
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?".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?".
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?".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?".
MLModelType
,
MLModelType
MLModel.Builder scoreThreshold(Float scoreThreshold)
scoreThreshold
- The new value for the ScoreThreshold property for this object.MLModel.Builder scoreThresholdLastUpdatedAt(Instant scoreThresholdLastUpdatedAt)
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
scoreThresholdLastUpdatedAt
- The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch
time.MLModel.Builder message(String message)
A description of the most recent details about accessing the MLModel
.
message
- A description of the most recent details about accessing the MLModel
.MLModel.Builder computeTime(Long computeTime)
computeTime
- The new value for the ComputeTime property for this object.MLModel.Builder finishedAt(Instant finishedAt)
finishedAt
- The new value for the FinishedAt property for this object.MLModel.Builder startedAt(Instant startedAt)
startedAt
- The new value for the StartedAt property for this object.Copyright © 2017 Amazon Web Services, Inc. All Rights Reserved.