java.lang.String mLModelId
The ID assigned to the MLModel
at creation.
Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+
java.lang.Boolean verbose
Specifies whether the GetMLModel
operation should return
Recipe
.
If true, Recipe
is returned.
If false, Recipe
is not returned.
java.lang.String mLModelId
The MLModel ID, which is same as the MLModelId
in the
request.
Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+
java.lang.String trainingDataSourceId
The ID of the training DataSource
.
Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+
java.lang.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.
Constraints:
Pattern: arn:aws:iam::[0-9]+:((user/.+)|(root))
java.util.Date createdAt
The time that the MLModel
was created. The time is expressed
in epoch time.
java.util.Date lastUpdatedAt
The time of the most recent edit to the MLModel
. The time is
expressed in epoch time.
java.lang.String name
A user-supplied name or description of the MLModel
.
Constraints:
Length: - 1024
java.lang.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.
Constraints:
Allowed Values: PENDING, INPROGRESS, FAILED, COMPLETED, DELETED
java.lang.Long sizeInBytes
Long integer type that is a 64-bit signed number.
RealtimeEndpointInfo endpointInfo
The current endpoint of the MLModel
java.util.Map<K,V> 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.
java.lang.String inputDataLocationS3
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
Constraints:
Length: - 2048
Pattern: s3://([^/]+)(/.*)?
java.lang.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?"
Constraints:
Allowed Values: REGRESSION, BINARY, MULTICLASS
java.lang.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
.
java.util.Date scoreThresholdLastUpdatedAt
The time of the most recent edit to the ScoreThreshold
. The
time is expressed in epoch time.
java.lang.String logUri
A link to the file that contains logs of the CreateMLModel
operation.
java.lang.String message
A description of the most recent details about accessing the
MLModel
.
Constraints:
Length: - 10240
java.lang.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.
java.util.Date 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.
java.util.Date 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.
java.lang.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.
Note: This parameter is provided as part of the verbose format.
Constraints:
Length: - 131071
java.lang.String schema
The schema used by all of the data files referenced by the
DataSource
.
Note: This parameter is provided as part of the verbose format.
Constraints:
Length: - 131071
java.lang.Integer code
java.lang.Integer code
java.lang.Integer code
java.lang.String predictedLabel
The prediction label for either a BINARY
or
MULTICLASS
MLModel
.
Constraints:
Length: 1 -
java.lang.Float predictedValue
The prediction value for REGRESSION
MLModel
.
java.util.Map<K,V> predictedScores
Provides the raw classification score corresponding to each label.
java.util.Map<K,V> details
Provides any additional details regarding the prediction.
java.lang.String mLModelId
A unique identifier of the MLModel
.
Constraints:
Length: 1 - 64
Pattern: [a-zA-Z0-9_.-]+
java.util.Map<K,V> record
A map of variable name-value pairs that represent an observation.
java.lang.String predictEndpoint
Constraints:
Length: - 2048
Pattern: https://[a-zA-Z0-9-.]*\.amazon(aws)?\.com[/]?
Prediction prediction
The output from a Predict
operation:
Details
- Contains the following attributes:
DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS
DetailsAttributes.ALGORITHM - SGD
PredictedLabel
- Present for either a BINARY
or
MULTICLASS
MLModel
request.
PredictedScores
- Contains the raw classification score
corresponding to each label.
PredictedValue
- Present for a REGRESSION
MLModel
request.
java.lang.Integer peakRequestsPerSecond
The maximum processing rate for the real-time endpoint for
MLModel
, measured in incoming requests per second.
java.util.Date createdAt
The time that the request to create the real-time endpoint for the
MLModel
was received. The time is expressed in epoch time.
java.lang.String endpointUrl
The URI that specifies where to send real-time prediction requests for
the MLModel
.
Note: The application must wait until the real-time endpoint is ready before using this URI.
Constraints:
Length: - 2048
Pattern: https://[a-zA-Z0-9-.]*\.amazon(aws)?\.com[/]?
java.lang.String endpointStatus
The current status of the real-time endpoint for the MLModel
. This element can have one of the following values:
NONE
- Endpoint does not exist or was previously deleted.
READY
- Endpoint is ready to be used for real-time
predictions.
UPDATING
- Updating/creating the endpoint.
Constraints:
Allowed Values: NONE, READY, UPDATING, FAILED
java.lang.Integer code