{ "version":"2.0", "metadata":{ "apiVersion":"2014-12-12", "endpointPrefix":"machinelearning", "jsonVersion":"1.1", "serviceFullName":"Amazon Machine Learning", "signatureVersion":"v4", "targetPrefix":"AmazonML_20141212", "protocol":"json" }, "documentation":"Definition of the public APIs exposed by Amazon Machine Learning", "operations":{ "CreateBatchPrediction":{ "name":"CreateBatchPrediction", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateBatchPredictionInput"}, "output":{ "shape":"CreateBatchPredictionOutput", "documentation":"
Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.
The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the GetBatchPrediction operation and checking the Status
parameter of the result.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" }, { "shape":"IdempotentParameterMismatchException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
" } ], "documentation":"Generates predictions for a group of observations. The observations to process exist in one or more data files referenced by a DataSource
. This operation creates a new BatchPrediction
, and uses an MLModel
and the data files referenced by the DataSource
as information sources.
CreateBatchPrediction
is an asynchronous operation. In response to CreateBatchPrediction
, Amazon Machine Learning (Amazon ML) immediately returns and sets the BatchPrediction
status to PENDING
. After the BatchPrediction
completes, Amazon ML sets the status to COMPLETED
.
You can poll for status updates by using the GetBatchPrediction operation and checking the Status
parameter of the result. After the COMPLETED
status appears, the results are available in the location specified by the OutputUri
parameter.
Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRDS operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status
parameter. You can inspect the Message
when Status
shows up as FAILED
. You can also check the progress of the copy operation by going to the DataPipeline
console and looking up the pipeline using the pipelineId from the describe call.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" }, { "shape":"IdempotentParameterMismatchException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
" } ], "documentation":"Creates a DataSource
object from an Amazon Relational Database Service (Amazon RDS). A DataSource
references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
CreateDataSourceFromRDS
is an asynchronous operation. In response to CreateDataSourceFromRDS
, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource
status to PENDING
. After the DataSource
is created and ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in COMPLETED
or PENDING
status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute of the GetDataSource operation response.
Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status
parameter.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" }, { "shape":"IdempotentParameterMismatchException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
" } ], "documentation":"Creates a DataSource
from Amazon Redshift. A DataSource
references data that can be used to perform either CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
CreateDataSourceFromRedshift
is an asynchronous operation. In response to CreateDataSourceFromRedshift
, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource
status to PENDING
. After the DataSource
is created and ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in COMPLETED
or PENDING
status can only be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute of the GetDataSource operation response.
The observations should exist in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery
. Amazon ML executes Unload command in Amazon Redshift to transfer the result set of SelectSqlQuery
to S3StagingLocation.
After the DataSource
is created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource
to train an MLModel
, the DataSource
requires another item -- a recipe. A recipe describes the observation variables that participate in training an MLModel
. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.
Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status
parameter.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" }, { "shape":"IdempotentParameterMismatchException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
" } ], "documentation":"Creates a DataSource
object. A DataSource
references data that can be used to perform CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.
CreateDataSourceFromS3
is an asynchronous operation. In response to CreateDataSourceFromS3
, Amazon Machine Learning (Amazon ML) immediately returns and sets the DataSource
status to PENDING
. After the DataSource
is created and ready for use, Amazon ML sets the Status
parameter to COMPLETED
. DataSource
in COMPLETED
or PENDING
status can only be used to perform CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.
If Amazon ML cannot accept the input source, it sets the Status
parameter to FAILED
and includes an error message in the Message
attribute of the GetDataSource operation response.
The observation data used in a DataSource
should be ready to use; that is, it should have a consistent structure, and missing data values should be kept to a minimum. The observation data must reside in one or more CSV files in an Amazon Simple Storage Service (Amazon S3) bucket, along with a schema that describes the data items by name and type. The same schema must be used for all of the data files referenced by the DataSource
.
After the DataSource
has been created, it's ready to use in evaluations and batch predictions. If you plan to use the DataSource
to train an MLModel
, the DataSource
requires another item: a recipe. A recipe describes the observation variables that participate in training an MLModel
. A recipe describes how each input variable will be used in training. Will the variable be included or excluded from training? Will the variable be manipulated, for example, combined with another variable, or split apart into word combinations? The recipe provides answers to these questions. For more information, see the Amazon Machine Learning Developer Guide.
Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.
CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvaluation operation and checking the Status
parameter.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" }, { "shape":"IdempotentParameterMismatchException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
" } ], "documentation":"Creates a new Evaluation
of an MLModel
. An MLModel
is evaluated on a set of observations associated to a DataSource
. Like a DataSource
for an MLModel
, the DataSource
for an Evaluation
contains values for the Target Variable. The Evaluation
compares the predicted result for each observation to the actual outcome and provides a summary so that you know how effective the MLModel
functions on the test data. Evaluation generates a relevant performance metric such as BinaryAUC, RegressionRMSE or MulticlassAvgFScore based on the corresponding MLModelType
: BINARY
, REGRESSION
or MULTICLASS
.
CreateEvaluation
is an asynchronous operation. In response to CreateEvaluation
, Amazon Machine Learning (Amazon ML) immediately returns and sets the evaluation status to PENDING
. After the Evaluation
is created and ready for use, Amazon ML sets the status to COMPLETED
.
You can use the GetEvaluation operation to check progress of the evaluation during the creation operation.
" }, "CreateMLModel":{ "name":"CreateMLModel", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"CreateMLModelInput"}, "output":{ "shape":"CreateMLModelOutput", "documentation":"Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.
The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status
parameter.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" }, { "shape":"IdempotentParameterMismatchException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
" } ], "documentation":"Creates a new MLModel
using the data files and the recipe as information sources.
An MLModel
is nearly immutable. Users can only update the MLModelName
and the ScoreThreshold
in an MLModel
without creating a new MLModel
.
CreateMLModel
is an asynchronous operation. In response to CreateMLModel
, Amazon Machine Learning (Amazon ML) immediately returns and sets the MLModel
status to PENDING
. After the MLModel
is created and ready for use, Amazon ML sets the status to COMPLETED
.
You can use the GetMLModel operation to check progress of the MLModel
during the creation operation.
CreateMLModel requires a DataSource
with computed statistics, which can be created by setting ComputeStatistics
to true
in CreateDataSourceFromRDS, CreateDataSourceFromS3, or CreateDataSourceFromRedshift operations.
Represents the output of an CreateRealtimeEndpoint operation.
The result contains the MLModelId
and the endpoint information for the MLModel
.
The endpoint information includes the URI of the MLModel
; that is, the location to send online prediction requests for the specified MLModel
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Creates a real-time endpoint for the MLModel
. The endpoint contains the URI of the MLModel
; that is, the location to send real-time prediction requests for the specified MLModel
.
Represents the output of a DeleteBatchPrediction operation.
You can use the GetBatchPrediction operation and check the value of the Status
parameter to see whether a BatchPrediction
is marked as DELETED
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Assigns the DELETED status to a BatchPrediction
, rendering it unusable.
After using the DeleteBatchPrediction
operation, you can use the GetBatchPrediction operation to verify that the status of the BatchPrediction
changed to DELETED.
Caution: The result of the DeleteBatchPrediction
operation is irreversible.
Represents the output of a DeleteDataSource operation.
" }, "errors":[ { "shape":"InvalidInputException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Assigns the DELETED status to a DataSource
, rendering it unusable.
After using the DeleteDataSource
operation, you can use the GetDataSource operation to verify that the status of the DataSource
changed to DELETED.
Caution: The results of the DeleteDataSource
operation are irreversible.
Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.
You can use the GetEvaluation operation and check the value of the Status
parameter to see whether an Evaluation
is marked as DELETED
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Assigns the DELETED
status to an Evaluation
, rendering it unusable.
After invoking the DeleteEvaluation
operation, you can use the GetEvaluation operation to verify that the status of the Evaluation
changed to DELETED
.
Caution: The results of the DeleteEvaluation
operation are irreversible.
Represents the output of a DeleteMLModel operation.
You can use the GetMLModel operation and check the value of the Status
parameter to see whether an MLModel
is marked as DELETED
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Assigns the DELETED status to an MLModel
, rendering it unusable.
After using the DeleteMLModel
operation, you can use the GetMLModel operation to verify that the status of the MLModel
changed to DELETED.
Caution: The result of the DeleteMLModel
operation is irreversible.
Represents the output of an DeleteRealtimeEndpoint operation.
The result contains the MLModelId
and the endpoint information for the MLModel
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Deletes a real time endpoint of an MLModel
.
Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPrediction
s.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Returns a list of BatchPrediction
operations that match the search criteria in the request.
Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Returns a list of DataSource
that match the search criteria in the request.
Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Returns a list of DescribeEvaluations
that match the search criteria in the request.
Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Returns a list of MLModel
that match the search criteria in the request.
Represents the output of a GetBatchPrediction operation and describes a BatchPrediction
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Returns a BatchPrediction
that includes detailed metadata, status, and data file information for a Batch Prediction
request.
Represents the output of a GetDataSource operation and describes a DataSource
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Returns a DataSource
that includes metadata and data file information, as well as the current status of the DataSource
.
GetDataSource
provides results in normal or verbose format. The verbose format adds the schema description and the list of files pointed to by the DataSource to the normal format.
Represents the output of a GetEvaluation operation and describes an Evaluation
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Returns an Evaluation
that includes metadata as well as the current status of the Evaluation
.
Represents the output of a GetMLModel operation, and provides detailed information about a MLModel
.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Returns an MLModel
that includes detailed metadata, and data source information as well as the current status of the MLModel
.
GetMLModel
provides results in normal or verbose format.
An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"LimitExceededException", "error":{"httpStatusCode":417}, "exception":true, "documentation":"The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource
.
An error on the server occurred when trying to process a request.
" }, { "shape":"PredictorNotMountedException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"The exception is thrown when a predict request is made to an unmounted MLModel
.
Generates a prediction for the observation using the specified ML Model
.
Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.
Represents the output of an UpdateBatchPrediction operation.
You can see the updated content by using the GetBatchPrediction operation.
" }, "errors":[ { "shape":"InvalidInputException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Updates the BatchPredictionName
of a BatchPrediction
.
You can use the GetBatchPrediction operation to view the contents of the updated data element.
" }, "UpdateDataSource":{ "name":"UpdateDataSource", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"UpdateDataSourceInput"}, "output":{ "shape":"UpdateDataSourceOutput", "documentation":"Represents the output of an UpdateDataSource operation.
You can see the updated content by using the GetBatchPrediction operation.
" }, "errors":[ { "shape":"InvalidInputException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Updates the DataSourceName
of a DataSource
.
You can use the GetDataSource operation to view the contents of the updated data element.
" }, "UpdateEvaluation":{ "name":"UpdateEvaluation", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"UpdateEvaluationInput"}, "output":{ "shape":"UpdateEvaluationOutput", "documentation":"Represents the output of an UpdateEvaluation operation.
You can see the updated content by using the GetEvaluation operation.
" }, "errors":[ { "shape":"InvalidInputException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Updates the EvaluationName
of an Evaluation
.
You can use the GetEvaluation operation to view the contents of the updated data element.
" }, "UpdateMLModel":{ "name":"UpdateMLModel", "http":{ "method":"POST", "requestUri":"/" }, "input":{"shape":"UpdateMLModelInput"}, "output":{ "shape":"UpdateMLModelOutput", "documentation":"Represents the output of an UpdateMLModel operation.
You can see the updated content by using the GetMLModel operation.
" }, "errors":[ { "shape":"InvalidInputException", "error":{"httpStatusCode":400}, "exception":true, "documentation":"An error on the client occurred. Typically, the cause is an invalid input value.
" }, { "shape":"ResourceNotFoundException", "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, { "shape":"InternalServerException", "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" } ], "documentation":"Updates the MLModelName
and the ScoreThreshold
of an MLModel
.
You can use the GetMLModel operation to view the contents of the updated data element.
" } }, "shapes":{ "Algorithm":{ "type":"string", "enum":["sgd"], "documentation":"The function used to train a MLModel
. Training choices supported by Amazon ML include the following:
An Amazon Web Service (AWS) user account identifier. The account identifier can be an AWS root account or an AWS Identity and Access Management (IAM) user.
" }, "BatchPrediction":{ "type":"structure", "members":{ "BatchPredictionId":{ "shape":"EntityId", "documentation":"The ID assigned to the BatchPrediction
at creation. This value should be identical to the value of the BatchPredictionID
in the request.
The ID of the MLModel
that generated predictions for the BatchPrediction
request.
The ID of the DataSource
that points to the group of observations to predict.
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
" }, "CreatedByIamUser":{ "shape":"AwsUserArn", "documentation":"The AWS user account that invoked the BatchPrediction
. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The time that the BatchPrediction
was created. The time is expressed in epoch time.
The time of the most recent edit to the BatchPrediction
. The time is expressed in epoch time.
A user-supplied name or description of the BatchPrediction
.
The status of the BatchPrediction
. This element can have one of the following values:
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to generate predictions for a batch of observations.INPROGRESS
- The process is underway.FAILED
- The request to peform a batch prediction did not run to completion. It is not usable.COMPLETED
- The batch prediction process completed successfully.DELETED
- The BatchPrediction
is marked as deleted. It is not usable.The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the \"outputURI\" field: ':', '//', '/./', '/../'.
" }, "Message":{ "shape":"Message", "documentation":"A description of the most recent details about processing the batch prediction request.
" } }, "documentation":"Represents the output of GetBatchPrediction operation.
The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.
" }, "BatchPredictionFilterVariable":{ "type":"string", "enum":[ "CreatedAt", "LastUpdatedAt", "Status", "Name", "IAMUser", "MLModelId", "DataSourceId", "DataURI" ], "documentation":"A list of the variables to use in searching or filtering BatchPrediction
.
CreatedAt
- Sets the search criteria to BatchPrediction
creation date.Status
- Sets the search criteria to BatchPrediction
status.Name
- Sets the search criteria to the contents of BatchPrediction
Name
.IAMUser
- Sets the search criteria to the user account that invoked the BatchPrediction
creation.MLModelId
- Sets the search criteria to the MLModel
used in the BatchPrediction
.DataSourceId
- Sets the search criteria to the DataSource
used in the BatchPrediction
.DataURI
- Sets the search criteria to the data file(s) used in the BatchPrediction
. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.The value specified in a filtering condition. The ComparatorValue
becomes the reference value when matching or evaluating data values in filtering and searching functions.
A user-supplied ID that uniquely identifies the BatchPrediction
.
A user-supplied name or description of the BatchPrediction
. BatchPredictionName
can only use the UTF-8 character set.
The ID of the MLModel
that will generate predictions for the group of observations.
The ID of the DataSource
that points to the group of observations to predict.
The location of an Amazon Simple Storage Service (Amazon S3) bucket or directory to store the batch prediction results. The following substrings are not allowed in the s3 key portion of the \"outputURI\" field: ':', '//', '/./', '/../'.
Amazon ML needs permissions to store and retrieve the logs on your behalf. For information about how to set permissions, see the Amazon Machine Learning Developer Guide.
" } } }, "CreateBatchPredictionOutput":{ "type":"structure", "members":{ "BatchPredictionId":{ "shape":"EntityId", "documentation":"A user-supplied ID that uniquely identifies the BatchPrediction
. This value is identical to the value of the BatchPredictionId
in the request.
Represents the output of a CreateBatchPrediction operation, and is an acknowledgement that Amazon ML received the request.
The CreateBatchPrediction operation is asynchronous. You can poll for status updates by using the GetBatchPrediction operation and checking the Status
parameter of the result.
A user-supplied ID that uniquely identifies the DataSource
. Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource
.
A user-supplied name or description of the DataSource
.
The data specification of an Amazon RDS DataSource
:
DatabaseInformation -
DatabaseName
- Name of the Amazon RDS database. InstanceIdentifier
- Unique identifier for the Amazon RDS database instance.DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.
ResourceRole - Role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (EC2) instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
ServiceRole - Role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon Simple Storage Service (S3). For more information, see Role templates for data pipelines.
SecurityInfo - Security information to use to access an Amazon RDS instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId
, SecurityGroupIds
] pair for a VPC-based Amazon RDS instance.
SelectSqlQuery - Query that is used to retrieve the observation data for the Datasource
.
S3StagingLocation - Amazon S3 location for staging RDS data. The data retrieved from Amazon RDS using SelectSqlQuery
is stored in this location.
DataSchemaUri - Amazon S3 location of the DataSchema
.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri
is specified.
DataRearrangement - A JSON string representing the splitting requirement of a Datasource
.
Sample - \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
The role that Amazon ML assumes on behalf of the user to create and activate a data pipeline in the user’s account and copy data (using the SelectSqlQuery
) query from Amazon RDS to Amazon S3.
" }, "ComputeStatistics":{ "shape":"ComputeStatistics", "documentation":"
The compute statistics for a DataSource
. The statistics are generated from the observation data referenced by a DataSource
. Amazon ML uses the statistics internally during an MLModel
training. This parameter must be set to true
if the DataSource
needs to be used for
MLModel
training.
A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID
in the request.
Represents the output of a CreateDataSourceFromRDS operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRDS operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status
parameter. You can inspect the Message
when Status
shows up as FAILED
. You can also check the progress of the copy operation by going to the DataPipeline
console and looking up the pipeline using the pipelineId from the describe call.
A user-supplied ID that uniquely identifies the DataSource
.
A user-supplied name or description of the DataSource
.
The data specification of an Amazon Redshift DataSource
:
DatabaseInformation -
DatabaseName
- Name of the Amazon Redshift database. ClusterIdentifier
- Unique ID for the Amazon Redshift cluster.DatabaseCredentials - AWS Identity abd Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.
SelectSqlQuery - Query that is used to retrieve the observation data for the Datasource
.
S3StagingLocation - Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Relational Database Service (Amazon RDS) using SelectSqlQuery
is stored in this location.
DataSchemaUri - Amazon S3 location of the DataSchema
.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri
is specified.
DataRearrangement - A JSON string representing the splitting requirement of a Datasource
.
Sample - \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:
A security group to allow Amazon ML to execute the SelectSqlQuery
query on an Amazon Redshift cluster
An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation
The compute statistics for a DataSource
. The statistics are generated from the observation data referenced by a DataSource
. Amazon ML uses the statistics internally during MLModel
training. This parameter must be set to true
if the DataSource
needs to be used for
MLModel
training
A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID
in the request.
Represents the output of a CreateDataSourceFromRedshift operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromRedshift operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status
parameter.
A user-supplied identifier that uniquely identifies the DataSource
.
A user-supplied name or description of the DataSource
.
The data specification of a DataSource
:
DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.
DataSchemaLocationS3 - Amazon S3 location of the DataSchema
.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri
is specified.
DataRearrangement - A JSON string representing the splitting requirement of a Datasource
.
Sample - \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
The compute statistics for a DataSource
. The statistics are generated from the observation data referenced by a DataSource
. Amazon ML uses the statistics internally during an MLModel
training. This parameter must be set to true
if the DataSource
needs to be used for
MLModel
training
A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID
in the request.
Represents the output of a CreateDataSourceFromS3 operation, and is an acknowledgement that Amazon ML received the request.
The CreateDataSourceFromS3 operation is asynchronous. You can poll for updates by using the GetBatchPrediction operation and checking the Status
parameter.
A user-supplied ID that uniquely identifies the Evaluation
.
A user-supplied name or description of the Evaluation
.
The ID of the MLModel
to evaluate.
The schema used in creating the MLModel
must match the schema of the DataSource
used in the Evaluation
.
The ID of the DataSource
for the evaluation. The schema of the DataSource
must match the schema used to create the MLModel
.
The user-supplied ID that uniquely identifies the Evaluation
. This value should be identical to the value of the EvaluationId
in the request.
Represents the output of a CreateEvaluation operation, and is an acknowledgement that Amazon ML received the request.
CreateEvaluation operation is asynchronous. You can poll for status updates by using the GetEvaluation operation and checking the Status
parameter.
A user-supplied ID that uniquely identifies the MLModel
.
A user-supplied name or description of the MLModel
.
The category of supervised learning that this MLModel
will address. Choose from the following types:
REGRESSION
if the MLModel
will be used to predict a numeric value.BINARY
if the MLModel
result has two possible values.MULTICLASS
if the MLModel
result has a limited number of values. For more information, see the Amazon Machine Learning Developer Guide.
" }, "Parameters":{ "shape":"TrainingParameters", "documentation":"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.l1RegularizationAmount
- Coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This 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 not to use L1 normalization. The parameter cannot be used when L2
is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount
- 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 valuseis a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1
is specified. Use this parameter sparingly.
sgd.maxPasses
- 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.maxMLModelSizeInBytes
- 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.
The DataSource
that points to the training data.
The data recipe for creating MLModel
. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel
recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
A user-supplied ID that uniquely identifies the MLModel
. This value should be identical to the value of the MLModelId
in the request.
Represents the output of a CreateMLModel operation, and is an acknowledgement that Amazon ML received the request.
The CreateMLModel operation is asynchronous. You can poll for status updates by using the GetMLModel operation and checking the Status
parameter.
The ID assigned to the MLModel
during creation.
A user-supplied ID that uniquely identifies the MLModel
. This value should be identical to the value of the MLModelId
in the request.
The endpoint information of the MLModel
Represents the output of an CreateRealtimeEndpoint operation.
The result contains the MLModelId
and the endpoint information for the MLModel
.
The endpoint information includes the URI of the MLModel
; that is, the location to send online prediction requests for the specified MLModel
.
The schema of a DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
. The DataSource schema is expressed in JSON format.
DataSchema
is not required if you specify a DataSchemaUri
{ \"version\": \"1.0\", \"recordAnnotationFieldName\": \"F1\", \"recordWeightFieldName\": \"F2\", \"targetFieldName\": \"F3\", \"dataFormat\": \"CSV\", \"dataFileContainsHeader\": true, \"variables\": [ { \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ], \"excludedVariableNames\": [ \"F6\" ] }
" }, "DataSource":{ "type":"structure", "members":{ "DataSourceId":{ "shape":"EntityId", "documentation":"The ID that is assigned to the DataSource
during creation.
The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource
.
A JSON string that represents the splitting requirement of a Datasource
.
The AWS user account from which the DataSource
was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The time that the DataSource
was created. The time is expressed in epoch time.
The time of the most recent edit to the BatchPrediction
. The time is expressed in epoch time.
The total number of observations contained in the data files that the DataSource
references.
The number of data files referenced by the DataSource
.
A user-supplied name or description of the DataSource
.
The current status of the DataSource
. This element can have one of the following values:
DataSource
.DataSource
did not run to completion. It is not usable.DataSource
is marked as deleted. It is not usable.A description of the most recent details about creating the DataSource
.
The parameter is true
if statistics need to be generated from the observation data.
Represents the output of the GetDataSource operation.
The content consists of the detailed metadata and data file information and the current status of the DataSource
.
A list of the variables to use in searching or filtering DataSource
.
CreatedAt
- Sets the search criteria to DataSource
creation date.Status
- Sets the search criteria to DataSource
status.Name
- Sets the search criteria to the contents of DataSource
Name
.DataUri
- Sets the search criteria to the URI of data files used to create the DataSource
. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.IAMUser
- Sets the search criteria to the user account that invoked the DataSource
creation.The variable names should match the variable names in the DataSource
.
A user-supplied ID that uniquely identifies the BatchPrediction
.
A user-supplied ID that uniquely identifies the BatchPrediction
. This value should be identical to the value of the BatchPredictionID
in the request.
Represents the output of a DeleteBatchPrediction operation.
You can use the GetBatchPrediction operation and check the value of the Status
parameter to see whether a BatchPrediction
is marked as DELETED
.
A user-supplied ID that uniquely identifies the DataSource
.
A user-supplied ID that uniquely identifies the DataSource
. This value should be identical to the value of the DataSourceID
in the request.
Represents the output of a DeleteDataSource operation.
" }, "DeleteEvaluationInput":{ "type":"structure", "required":["EvaluationId"], "members":{ "EvaluationId":{ "shape":"EntityId", "documentation":"A user-supplied ID that uniquely identifies the Evaluation
to delete.
A user-supplied ID that uniquely identifies the Evaluation
. This value should be identical to the value of the EvaluationId
in the request.
Represents the output of a DeleteEvaluation operation. The output indicates that Amazon Machine Learning (Amazon ML) received the request.
You can use the GetEvaluation operation and check the value of the Status
parameter to see whether an Evaluation
is marked as DELETED
.
A user-supplied ID that uniquely identifies the MLModel
.
A user-supplied ID that uniquely identifies the MLModel
. This value should be identical to the value of the MLModelID
in the request.
Represents the output of a DeleteMLModel operation.
You can use the GetMLModel operation and check the value of the Status
parameter to see whether an MLModel
is marked as DELETED
.
The ID assigned to the MLModel
during creation.
A user-supplied ID that uniquely identifies the MLModel
. This value should be identical to the value of the MLModelId
in the request.
The endpoint information of the MLModel
Represents the output of an DeleteRealtimeEndpoint operation.
The result contains the MLModelId
and the endpoint information for the MLModel
.
Use one of the following variables to filter a list of BatchPrediction
:
CreatedAt
- Sets the search criteria to the BatchPrediction
creation date.Status
- Sets the search criteria to the BatchPrediction
status.Name
- Sets the search criteria to the contents of the BatchPrediction
Name
.IAMUser
- Sets the search criteria to the user account that invoked the BatchPrediction
creation.MLModelId
- Sets the search criteria to the MLModel
used in the BatchPrediction
.DataSourceId
- Sets the search criteria to the DataSource
used in the BatchPrediction
.DataURI
- Sets the search criteria to the data file(s) used in the BatchPrediction
. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.The equal to operator. The BatchPrediction
results will have FilterVariable
values that exactly match the value specified with EQ
.
The greater than operator. The BatchPrediction
results will have FilterVariable
values that are greater than the value specified with GT
.
The less than operator. The BatchPrediction
results will have FilterVariable
values that are less than the value specified with LT
.
The greater than or equal to operator. The BatchPrediction
results will have FilterVariable
values that are greater than or equal to the value specified with GE
.
The less than or equal to operator. The BatchPrediction
results will have FilterVariable
values that are less than or equal to the value specified with LE
.
The not equal to operator. The BatchPrediction
results will have FilterVariable
values not equal to the value specified with NE
.
A string that is found at the beginning of a variable, such as Name
or Id
.
For example, a Batch Prediction
operation could have the Name
2014-09-09-HolidayGiftMailer
. To search for this BatchPrediction
, select Name
for the FilterVariable
and any of the following strings for the Prefix
:
2014-09
2014-09-09
2014-09-09-Holiday
A two-value parameter that determines the sequence of the resulting list of MLModel
s.
asc
- Arranges the list in ascending order (A-Z, 0-9).dsc
- Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable
.
An ID of the page in the paginated results.
" }, "Limit":{ "shape":"PageLimit", "documentation":"The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.
" } } }, "DescribeBatchPredictionsOutput":{ "type":"structure", "members":{ "Results":{ "shape":"BatchPredictions", "documentation":"A list of BatchPrediction objects that meet the search criteria.
" }, "NextToken":{ "shape":"StringType", "documentation":"The ID of the next page in the paginated results that indicates at least one more page follows.
" } }, "documentation":"Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPrediction
s.
Use one of the following variables to filter a list of DataSource
:
CreatedAt
- Sets the search criteria to DataSource
creation dates.Status
- Sets the search criteria to DataSource
statuses.Name
- Sets the search criteria to the contents of DataSource
Name
.DataUri
- Sets the search criteria to the URI of data files used to create the DataSource
. The URI can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.IAMUser
- Sets the search criteria to the user account that invoked the DataSource
creation.The equal to operator. The DataSource
results will have FilterVariable
values that exactly match the value specified with EQ
.
The greater than operator. The DataSource
results will have FilterVariable
values that are greater than the value specified with GT
.
The less than operator. The DataSource
results will have FilterVariable
values that are less than the value specified with LT
.
The greater than or equal to operator. The DataSource
results will have FilterVariable
values that are greater than or equal to the value specified with GE
.
The less than or equal to operator. The DataSource
results will have FilterVariable
values that are less than or equal to the value specified with LE
.
The not equal to operator. The DataSource
results will have FilterVariable
values not equal to the value specified with NE
.
A string that is found at the beginning of a variable, such as Name
or Id
.
For example, a DataSource
could have the Name
2014-09-09-HolidayGiftMailer
. To search for this DataSource
, select Name
for the FilterVariable
and any of the following strings for the Prefix
:
2014-09
2014-09-09
2014-09-09-Holiday
A two-value parameter that determines the sequence of the resulting list of DataSource
.
asc
- Arranges the list in ascending order (A-Z, 0-9).dsc
- Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable
.
The ID of the page in the paginated results.
" }, "Limit":{ "shape":"PageLimit", "documentation":" The maximum number of DataSource
to include in the result.
A list of DataSource
that meet the search criteria.
An ID of the next page in the paginated results that indicates at least one more page follows.
" } }, "documentation":"Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource
.
Use one of the following variable to filter a list of Evaluation
objects:
CreatedAt
- Sets the search criteria to the Evaluation
creation date.Status
- Sets the search criteria to the Evaluation
status.Name
- Sets the search criteria to the contents of Evaluation
Name
.IAMUser
- Sets the search criteria to the user account that invoked an Evaluation
.MLModelId
- Sets the search criteria to the MLModel
that was evaluated.DataSourceId
- Sets the search criteria to the DataSource
used in Evaluation
.DataUri
- Sets the search criteria to the data file(s) used in Evaluation
. The URL can identify either a file or an Amazon Simple Storage Solution (Amazon S3) bucket or directory.The equal to operator. The Evaluation
results will have FilterVariable
values that exactly match the value specified with EQ
.
The greater than operator. The Evaluation
results will have FilterVariable
values that are greater than the value specified with GT
.
The less than operator. The Evaluation
results will have FilterVariable
values that are less than the value specified with LT
.
The greater than or equal to operator. The Evaluation
results will have FilterVariable
values that are greater than or equal to the value specified with GE
.
The less than or equal to operator. The Evaluation
results will have FilterVariable
values that are less than or equal to the value specified with LE
.
The not equal to operator. The Evaluation
results will have FilterVariable
values not equal to the value specified with NE
.
A string that is found at the beginning of a variable, such as Name
or Id
.
For example, an Evaluation
could have the Name
2014-09-09-HolidayGiftMailer
. To search for this Evaluation
, select Name
for the FilterVariable
and any of the following strings for the Prefix
:
2014-09
2014-09-09
2014-09-09-Holiday
A two-value parameter that determines the sequence of the resulting list of Evaluation
.
asc
- Arranges the list in ascending order (A-Z, 0-9).dsc
- Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable
.
The ID of the page in the paginated results.
" }, "Limit":{ "shape":"PageLimit", "documentation":" The maximum number of Evaluation
to include in the result.
A list of Evaluation that meet the search criteria.
" }, "NextToken":{ "shape":"StringType", "documentation":"The ID of the next page in the paginated results that indicates at least one more page follows.
" } }, "documentation":"Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation
.
Use one of the following variables to filter a list of MLModel
:
CreatedAt
- Sets the search criteria to MLModel
creation date.Status
- Sets the search criteria to MLModel
status.Name
- Sets the search criteria to the contents of MLModel
Name
.IAMUser
- Sets the search criteria to the user account that invoked the MLModel
creation.TrainingDataSourceId
- Sets the search criteria to the DataSource
used to train one or more MLModel
.RealtimeEndpointStatus
- Sets the search criteria to the MLModel
real-time endpoint status.MLModelType
- Sets the search criteria to MLModel
type: binary, regression, or multi-class.Algorithm
- Sets the search criteria to the algorithm that the MLModel
uses.TrainingDataURI
- Sets the search criteria to the data file(s) used in training a MLModel
. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.The equal to operator. The MLModel
results will have FilterVariable
values that exactly match the value specified with EQ
.
The greater than operator. The MLModel
results will have FilterVariable
values that are greater than the value specified with GT
.
The less than operator. The MLModel
results will have FilterVariable
values that are less than the value specified with LT
.
The greater than or equal to operator. The MLModel
results will have FilterVariable
values that are greater than or equal to the value specified with GE
.
The less than or equal to operator. The MLModel
results will have FilterVariable
values that are less than or equal to the value specified with LE
.
The not equal to operator. The MLModel
results will have FilterVariable
values not equal to the value specified with NE
.
A string that is found at the beginning of a variable, such as Name
or Id
.
For example, an MLModel
could have the Name
2014-09-09-HolidayGiftMailer
. To search for this MLModel
, select Name
for the FilterVariable
and any of the following strings for the Prefix
:
2014-09
2014-09-09
2014-09-09-Holiday
A two-value parameter that determines the sequence of the resulting list of MLModel
.
asc
- Arranges the list in ascending order (A-Z, 0-9).dsc
- Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable
.
The ID of the page in the paginated results.
" }, "Limit":{ "shape":"PageLimit", "documentation":"The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.
" } } }, "DescribeMLModelsOutput":{ "type":"structure", "members":{ "Results":{ "shape":"MLModels", "documentation":"A list of MLModel that meet the search criteria.
" }, "NextToken":{ "shape":"StringType", "documentation":"The ID of the next page in the paginated results that indicates at least one more page follows.
" } }, "documentation":"Represents the output of a DescribeMLModels operation. The content is essentially a list of MLModel
.
DetailsMap
: PredictiveModelType - Indicates the type of the MLModel
. Algorithm - Indicates the algorithm was used for the MLModel
."
},
"DetailsMap":{
"type":"map",
"key":{"shape":"DetailsAttributes"},
"value":{"shape":"DetailsValue"},
"documentation":"Provides any additional details regarding the prediction."
},
"DetailsValue":{
"type":"string",
"min":1
},
"EDPPipelineId":{
"type":"string",
"min":1,
"max":1024
},
"EDPResourceRole":{
"type":"string",
"min":1,
"max":64
},
"EDPSecurityGroupId":{
"type":"string",
"min":1,
"max":255
},
"EDPSecurityGroupIds":{
"type":"list",
"member":{"shape":"EDPSecurityGroupId"}
},
"EDPServiceRole":{
"type":"string",
"min":1,
"max":64
},
"EDPSubnetId":{
"type":"string",
"min":1,
"max":255
},
"EntityId":{
"type":"string",
"min":1,
"max":64,
"pattern":"[a-zA-Z0-9_.-]+"
},
"EntityName":{
"type":"string",
"max":1024,
"pattern":".*\\S.*|^$",
"documentation":"A user-supplied name or description of the Amazon ML resource.
" }, "EntityStatus":{ "type":"string", "enum":[ "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED" ], "documentation":"Entity status with the following possible values:
A timestamp represented in epoch time.
" }, "ErrorCode":{"type":"integer"}, "ErrorMessage":{ "type":"string", "max":2048 }, "Evaluation":{ "type":"structure", "members":{ "EvaluationId":{ "shape":"EntityId", "documentation":"The ID that is assigned to the Evaluation
at creation.
The ID of the MLModel
that is the focus of the evaluation.
The ID of the DataSource
that is used to evaluate the MLModel
.
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
" }, "CreatedByIamUser":{ "shape":"AwsUserArn", "documentation":"The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
" }, "CreatedAt":{ "shape":"EpochTime", "documentation":"The time that the Evaluation
was created. The time is expressed in epoch time.
The time of the most recent edit to the Evaluation
. The time is expressed in epoch time.
A user-supplied name or description of the Evaluation
.
The status of the evaluation. This element can have one of the following values:
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to evaluate an MLModel
.INPROGRESS
- The evaluation is underway.FAILED
- The request to evaluate an MLModel
did not run to completion. It is not usable.COMPLETED
- The evaluation process completed successfully.DELETED
- The Evaluation
is marked as deleted. It is not usable.Measurements of how well the MLModel
performed, using observations referenced by the DataSource
. One of the following metrics is returned, based on the type of the MLModel:
BinaryAUC: A binary MLModel
uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: A regression MLModel
uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: A multiclass MLModel
uses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
" }, "Message":{ "shape":"Message", "documentation":"A description of the most recent details about evaluating the MLModel
.
Represents the output of GetEvaluation operation.
The content consists of the detailed metadata and data file information and the current status of the Evaluation
.
A list of the variables to use in searching or filtering Evaluation
.
CreatedAt
- Sets the search criteria to Evaluation
creation date.Status
- Sets the search criteria to Evaluation
status.Name
- Sets the search criteria to the contents of Evaluation
Name
.IAMUser
- Sets the search criteria to the user account that invoked an evaluation.MLModelId
- Sets the search criteria to the Predictor
that was evaluated.DataSourceId
- Sets the search criteria to the DataSource
used in evaluation.DataUri
- Sets the search criteria to the data file(s) used in evaluation. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.An ID assigned to the BatchPrediction
at creation.
An ID assigned to the BatchPrediction
at creation. This value should be identical to the value of the BatchPredictionID
in the request.
The ID of the MLModel
that generated predictions for the BatchPrediction
request.
The ID of the DataSource
that was used to create the BatchPrediction
.
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
" }, "CreatedByIamUser":{ "shape":"AwsUserArn", "documentation":"The AWS user account that invoked the BatchPrediction
. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The time when the BatchPrediction
was created. The time is expressed in epoch time.
The time of the most recent edit to BatchPrediction
. The time is expressed in epoch time.
A user-supplied name or description of the BatchPrediction
.
The status of the BatchPrediction
, which can be one of the following values:
PENDING
- Amazon Machine Learning (Amazon ML) submitted a request to generate batch predictions.INPROGRESS
- The batch predictions are in progress.FAILED
- The request to perform a batch prediction did not run to completion. It is not usable.COMPLETED
- The batch prediction process completed successfully.DELETED
- The BatchPrediction
is marked as deleted. It is not usable.The location of an Amazon S3 bucket or directory to receive the operation results.
" }, "LogUri":{ "shape":"PresignedS3Url", "documentation":"A link to the file that contains logs of the CreateBatchPrediction operation.
" }, "Message":{ "shape":"Message", "documentation":"A description of the most recent details about processing the batch prediction request.
" } }, "documentation":"Represents the output of a GetBatchPrediction operation and describes a BatchPrediction
.
The ID assigned to the DataSource
at creation.
Specifies whether the GetDataSource
operation should return DataSourceSchema
.
If true, DataSourceSchema
is returned.
If false, DataSourceSchema
is not returned.
The ID assigned to the DataSource
at creation. This value should be identical to the value of the DataSourceId
in the request.
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
" }, "DataRearrangement":{ "shape":"DataRearrangement", "documentation":"A JSON string that captures the splitting rearrangement requirement of the DataSource
.
The AWS user account from which the DataSource
was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The time that the DataSource
was created. The time is expressed in epoch time.
The time of the most recent edit to the DataSource
. The time is expressed in epoch time.
The total size of observations in the data files.
" }, "NumberOfFiles":{ "shape":"LongType", "documentation":"The number of data files referenced by the DataSource
.
A user-supplied name or description of the DataSource
.
The current status of the DataSource
. This element can have one of the following values:
PENDING
- Amazon Machine Language (Amazon ML) submitted a request to create a DataSource
.INPROGRESS
- The creation process is underway.FAILED
- The request to create a DataSource
did not run to completion. It is not usable.COMPLETED
- The creation process completed successfully.DELETED
- The DataSource
is marked as deleted. It is not usable.A link to the file containining logs of either create DataSource
operation.
The description of the most recent details about creating the DataSource
.
The parameter is true
if statistics need to be generated from the observation data.
The schema used by all of the data files of this DataSource
.
This parameter is provided as part of the verbose format.
Represents the output of a GetDataSource operation and describes a DataSource
.
The ID of the Evaluation
to retrieve. The evaluation of each MLModel
is recorded and cataloged. The ID provides the means to access the information.
The evaluation ID which is same as the EvaluationId
in the request.
The ID of the MLModel
that was the focus of the evaluation.
The DataSource
used for this evaluation.
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
" }, "CreatedByIamUser":{ "shape":"AwsUserArn", "documentation":"The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
" }, "CreatedAt":{ "shape":"EpochTime", "documentation":"The time that the Evaluation
was created. The time is expressed in epoch time.
The time of the most recent edit to the BatchPrediction
. The time is expressed in epoch time.
A user-supplied name or description of the Evaluation
.
The status of the evaluation. This element can have one of the following values:
PENDING
- Amazon Machine Language (Amazon ML) submitted a request to evaluate an MLModel
.INPROGRESS
- The evaluation is underway.FAILED
- The request to evaluate an MLModel
did not run to completion. It is not usable.COMPLETED
- The evaluation process completed successfully.DELETED
- The Evaluation
is marked as deleted. It is not usable.Measurements of how well the MLModel
performed using observations referenced by the DataSource
. One of the following metric is returned based on the type of the MLModel
:
BinaryAUC: A binary MLModel
uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: A regression MLModel
uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: A multiclass MLModel
uses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
" }, "LogUri":{ "shape":"PresignedS3Url", "documentation":"A link to the file that contains logs of the CreateEvaluation operation.
" }, "Message":{ "shape":"Message", "documentation":"A description of the most recent details about evaluating the MLModel
.
Represents the output of a GetEvaluation operation and describes an Evaluation
.
The ID assigned to the MLModel
at creation.
Specifies whether the GetMLModel
operation should return Recipe
.
If true, Recipe
is returned.
If false, Recipe
is not returned.
The MLModel ID which is same as the MLModelId
in the request.
The ID of the training DataSource
.
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.
The time that the MLModel
was created. The time is expressed in epoch time.
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
A user-supplied name or description of the MLModel
.
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. It is not usable.COMPLETED
- The request completed successfully.DELETED
- The MLModel
is marked as deleted. It is not usable.The current endpoint of the MLModel
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.l1RegularizationAmount
- 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, specify a small value, such as 1.0E-04 or 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2
is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount
- 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, specify a small value, such as 1.0E-04 or 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This parameter cannot be used when L1
is specified. Use this parameter sparingly.
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.maxMLModelSizeInBytes
- The maximum allowed size of the model. Depending on the input data, the model size might affect performance.
The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
" }, "MLModelType":{ "shape":"MLModelType", "documentation":"Identifies the MLModel
category. The following are the available types:
The scoring threshold is used in binary classification MLModel
s, and 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
.
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
A link to the file that contains logs of the CreateMLModel
operation.
Description of the most recent details about accessing the MLModel
.
The recipe to use when training the MLModel
. The Recipe
provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.
This parameter is provided as part of the verbose format.
The schema used by all of the data files referenced by the DataSource
.
This parameter is provided as part of the verbose format.
Represents the output of a GetMLModel operation, and provides detailed information about a MLModel
.
A second request to use or change an object was not allowed. This can result from retrying a request using a parameter that was not present in the original request.
" }, "IntegerType":{ "type":"integer", "documentation":"Integer type that is a 32-bit signed number.
" }, "InternalServerException":{ "type":"structure", "members":{ "message":{"shape":"ErrorMessage"}, "code":{"shape":"ErrorCode"} }, "error":{"httpStatusCode":500}, "exception":true, "fault":true, "documentation":"An error on the server occurred when trying to process a request.
" }, "InvalidInputException":{ "type":"structure", "members":{ "message":{"shape":"ErrorMessage"}, "code":{"shape":"ErrorCode"} }, "error":{"httpStatusCode":400}, "exception":true, "documentation":"An error on the client occurred. Typically, the cause is an invalid input value.
" }, "Label":{ "type":"string", "min":1 }, "LimitExceededException":{ "type":"structure", "members":{ "message":{"shape":"ErrorMessage"}, "code":{"shape":"ErrorCode"} }, "error":{"httpStatusCode":417}, "exception":true, "documentation":"The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource
.
Long integer type that is a 64-bit signed number.
" }, "MLModel":{ "type":"structure", "members":{ "MLModelId":{ "shape":"EntityId", "documentation":"The ID assigned to the MLModel
at creation.
The ID of the training DataSource
. The CreateMLModel operation uses the TrainingDataSourceId
.
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.
The time that the MLModel
was created. The time is expressed in epoch time.
The time of the most recent edit to the MLModel
. The time is expressed in epoch time.
A user-supplied name or description of the MLModel
.
The current status of an MLModel
. This element can have one of the following values:
MLModel
.MLModel
did not run to completion. It is not usable.MLModel
is marked as deleted. It is not usable.The current endpoint of the MLModel
.
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.l1RegularizationAmount
- 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, specify a small value, such as 1.0E-04 or 1.0E-08.
The value is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L1 normalization. The parameter cannot be used when L2
is specified. Use this parameter sparingly.
sgd.l2RegularizationAmount
- 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, specify a small value, such as 1.0E-04 or 1.0E-08.
The valus is a double that ranges from 0 to MAX_DOUBLE. The default is not to use L2 normalization. This cannot be used when L1
is specified. Use this parameter sparingly.
sgd.maxPasses
- 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.maxMLModelSizeInBytes
- Maximum allowed size of the model. Depending on the input data, the model size might affect performance.
The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
" }, "Algorithm":{ "shape":"Algorithm", "documentation":"The algorithm used to train the MLModel
. The following algorithm is supported:
Identifies the MLModel
category. The following are the available types:
The time of the most recent edit to the ScoreThreshold
. The time is expressed in epoch time.
A description of the most recent details about accessing the MLModel
.
Represents the output of a GetMLModel operation.
The content consists of the detailed metadata and the current status of the MLModel
.
Description of the most recent details about an entity.
" }, "PageLimit":{ "type":"integer", "min":1, "max":100 }, "PerformanceMetrics":{ "type":"structure", "members":{ "Properties":{"shape":"PerformanceMetricsProperties"} }, "documentation":"Measurements of how well the MLModel
performed on known observations. One of the following metrics is returned, based on the type of the MLModel
:
BinaryAUC: The binary MLModel
uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: The regression MLModel
uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: The multiclass MLModel
uses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
" }, "PerformanceMetricsProperties":{ "type":"map", "key":{"shape":"PerformanceMetricsPropertyKey"}, "value":{"shape":"PerformanceMetricsPropertyValue"} }, "PerformanceMetricsPropertyKey":{"type":"string"}, "PerformanceMetricsPropertyValue":{"type":"string"}, "PredictInput":{ "type":"structure", "required":[ "MLModelId", "Record", "PredictEndpoint" ], "members":{ "MLModelId":{ "shape":"EntityId", "documentation":"A unique identifier of the MLModel
.
MLModel
."
},
"predictedValue":{
"shape":"floatLabel",
"documentation":"The prediction value for REGRESSION MLModel
."
},
"predictedScores":{"shape":"ScoreValuePerLabelMap"},
"details":{"shape":"DetailsMap"}
},
"documentation":"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.
The exception is thrown when a predict request is made to an unmounted MLModel
.
Describes the DatabaseName
and InstanceIdentifier
of an an Amazon RDS database.
The query that is used to retrieve the observation data for the DataSource
.
The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
" }, "S3StagingLocation":{ "shape":"S3Url", "documentation":"The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery
is stored in this location.
DataRearrangement - A JSON string that represents the splitting requirement of a DataSource
.
Sample - \"{\\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
A JSON string that represents the schema for an Amazon RDS DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
.
A DataSchema
is not required if you specify a DataSchemaUri
Define your DataSchema
as a series of key-value pairs. attributes
and excludedVariableNames
have an array of key-value pairs for their value. Use the following format to define your DataSchema
.
{ \"version\": \"1.0\",
\"recordAnnotationFieldName\": \"F1\",
\"recordWeightFieldName\": \"F2\",
\"targetFieldName\": \"F3\",
\"dataFormat\": \"CSV\",
\"dataFileContainsHeader\": true,
\"attributes\": [
{ \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ],
\"excludedVariableNames\": [ \"F6\" ] }
" }, "DataSchemaUri":{ "shape":"S3Url", "documentation":"The Amazon S3 location of the DataSchema
.
The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
" }, "ServiceRole":{ "shape":"EDPServiceRole", "documentation":"The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
" }, "SubnetId":{ "shape":"EDPSubnetId", "documentation":"The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.
" }, "SecurityGroupIds":{ "shape":"EDPSecurityGroupIds", "documentation":"The security group IDs to be used to access a VPC-based RDS DB instance. Ensure that there are appropriate ingress rules set up to allow access to the RDS DB instance. This attribute is used by Data Pipeline to carry out the copy operation from Amazon RDS to an Amazon S3 task.
" } }, "documentation":"The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource
.
The ID of an RDS DB instance.
" }, "DatabaseName":{"shape":"RDSDatabaseName"} }, "documentation":"The database details of an Amazon RDS database.
" }, "RDSDatabaseCredentials":{ "type":"structure", "required":[ "Username", "Password" ], "members":{ "Username":{"shape":"RDSDatabaseUsername"}, "Password":{"shape":"RDSDatabasePassword"} }, "documentation":"The database credentials to connect to a database on an RDS DB instance.
" }, "RDSDatabaseName":{ "type":"string", "min":1, "max":64, "documentation":"The name of a database hosted on an RDS DB instance.
" }, "RDSDatabasePassword":{ "type":"string", "min":8, "max":128, "documentation":"The password to be used by Amazon ML to connect to a database on an RDS DB instance. The password should have sufficient permissions to execute the RDSSelectQuery
query.
The username to be used by Amazon ML to connect to database on an Amazon RDS instance. The username should have sufficient permissions to execute an RDSSelectSqlQuery
query.
The database details required to connect to an Amazon RDS.
" }, "DatabaseUserName":{"shape":"RDSDatabaseUsername"}, "SelectSqlQuery":{ "shape":"RDSSelectSqlQuery", "documentation":"The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose
is true in GetDataSourceInput
.
The role (DataPipelineDefaultResourceRole) assumed by an Amazon EC2 instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
" }, "ServiceRole":{ "shape":"EDPServiceRole", "documentation":"The role (DataPipelineDefaultRole) assumed by the Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
" }, "DataPipelineId":{ "shape":"EDPPipelineId", "documentation":"The ID of the Data Pipeline instance that is used to carry to copy data from Amazon RDS to Amazon S3. You can use the ID to find details about the instance in the Data Pipeline console.
" } }, "documentation":"The datasource details that are specific to Amazon RDS.
" }, "RDSSelectSqlQuery":{ "type":"string", "min":1, "max":16777216, "documentation":"The SQL query to be executed against the Amazon RDS database. The SQL query should be valid for the Amazon RDS type being used.
" }, "RealtimeEndpointInfo":{ "type":"structure", "members":{ "PeakRequestsPerSecond":{ "shape":"IntegerType", "documentation":" The maximum processing rate for the real-time endpoint for MLModel
, measured in incoming requests per second.
The time that the request to create the real-time endpoint for the MLModel
was received. The time is expressed in epoch time.
The URI that specifies where to send real-time prediction requests for the MLModel
.
The application must wait until the real-time endpoint is ready before using this URI.
The current status of the real-time endpoint for the MLModel
. This element can have one of the following values:
Describes the real-time endpoint information for an MLModel
.
A map of variable name-value pairs that represent an observation.
" }, "RedshiftClusterIdentifier":{ "type":"string", "min":1, "max":63, "pattern":"[a-z0-9-]+", "documentation":"The ID of an Amazon Redshift cluster.
" }, "RedshiftDataSpec":{ "type":"structure", "required":[ "DatabaseInformation", "SelectSqlQuery", "DatabaseCredentials", "S3StagingLocation" ], "members":{ "DatabaseInformation":{ "shape":"RedshiftDatabase", "documentation":"Describes the DatabaseName
and ClusterIdentifier
for an Amazon Redshift DataSource
.
Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource
.
Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
" }, "S3StagingLocation":{ "shape":"S3Url", "documentation":"Describes an Amazon S3 location to store the result set of the SelectSqlQuery
query.
Describes the splitting specifications for a DataSource
.
A JSON string that represents the schema for an Amazon Redshift DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
.
A DataSchema
is not required if you specify a DataSchemaUri
.
Define your DataSchema
as a series of key-value pairs. attributes
and excludedVariableNames
have an array of key-value pairs for their value. Use the following format to define your DataSchema
.
{ \"version\": \"1.0\",
\"recordAnnotationFieldName\": \"F1\",
\"recordWeightFieldName\": \"F2\",
\"targetFieldName\": \"F3\",
\"dataFormat\": \"CSV\",
\"dataFileContainsHeader\": true,
\"attributes\": [
{ \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ],
\"excludedVariableNames\": [ \"F6\" ] }
" }, "DataSchemaUri":{ "shape":"S3Url", "documentation":"Describes the schema location for an Amazon Redshift DataSource
.
Describes the data specification of an Amazon Redshift DataSource
.
Describes the database details required to connect to an Amazon Redshift database.
" }, "RedshiftDatabaseCredentials":{ "type":"structure", "required":[ "Username", "Password" ], "members":{ "Username":{"shape":"RedshiftDatabaseUsername"}, "Password":{"shape":"RedshiftDatabasePassword"} }, "documentation":"Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
" }, "RedshiftDatabaseName":{ "type":"string", "min":1, "max":64, "pattern":"[a-z0-9]+", "documentation":"The name of a database hosted on an Amazon Redshift cluster.
" }, "RedshiftDatabasePassword":{ "type":"string", "min":8, "max":64, "documentation":"A password to be used by Amazon ML to connect to a database on an Amazon Redshift cluster. The password should have sufficient permissions to execute a RedshiftSelectSqlQuery
query. The password should be valid for an Amazon Redshift USER.
A username to be used by Amazon Machine Learning (Amazon ML)to connect to a database on an Amazon Redshift cluster. The username should have sufficient permissions to execute the RedshiftSelectSqlQuery
query. The username should be valid for an Amazon Redshift USER.
The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose
is true in GetDataSourceInput.
Describes the DataSource
details specific to Amazon Redshift.
Describes the SQL query to execute on the Amazon Redshift database. The SQL query should be valid for an Amazon Redshift SELECT.
" }, "ResourceNotFoundException":{ "type":"structure", "members":{ "message":{"shape":"ErrorMessage"}, "code":{"shape":"ErrorCode"} }, "error":{"httpStatusCode":404}, "exception":true, "documentation":"A specified resource cannot be located.
" }, "RoleARN":{ "type":"string", "min":1, "max":100, "documentation":"The Amazon Resource Name (ARN) of an AWS IAM Role such as the following: arn:aws:iam::account:role/rolename.
" }, "S3DataSpec":{ "type":"structure", "required":["DataLocationS3"], "members":{ "DataLocationS3":{ "shape":"S3Url", "documentation":"The location of the data file(s) used by a DataSource
. The URI specifies a data file or an Amazon Simple Storage Service (Amazon S3) directory or bucket containing data files.
Describes the splitting requirement of a Datasource
.
A JSON string that represents the schema for an Amazon S3 DataSource
. The DataSchema
defines the structure of the observation data in the data file(s) referenced in the DataSource
.
Define your DataSchema
as a series of key-value pairs. attributes
and excludedVariableNames
have an array of key-value pairs for their value. Use the following format to define your DataSchema
.
{ \"version\": \"1.0\",
\"recordAnnotationFieldName\": \"F1\",
\"recordWeightFieldName\": \"F2\",
\"targetFieldName\": \"F3\",
\"dataFormat\": \"CSV\",
\"dataFileContainsHeader\": true,
\"attributes\": [
{ \"fieldName\": \"F1\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F2\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F3\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F4\", \"fieldType\": \"NUMERIC\" }, { \"fieldName\": \"F5\", \"fieldType\": \"CATEGORICAL\" }, { \"fieldName\": \"F6\", \"fieldType\": \"TEXT\" }, { \"fieldName\": \"F7\", \"fieldType\": \"WEIGHTED_INT_SEQUENCE\" }, { \"fieldName\": \"F8\", \"fieldType\": \"WEIGHTED_STRING_SEQUENCE\" } ],
\"excludedVariableNames\": [ \"F6\" ] }
" }, "DataSchemaLocationS3":{ "shape":"S3Url", "documentation":"Describes the schema Location in Amazon S3.
" } }, "documentation":" Describes the data specification of a DataSource
.
A reference to a file or bucket on Amazon Simple Storage Service (Amazon S3).
" }, "ScoreThreshold":{"type":"float"}, "ScoreValue":{"type":"float"}, "ScoreValuePerLabelMap":{ "type":"map", "key":{"shape":"Label"}, "value":{"shape":"ScoreValue"}, "documentation":"Provides the raw classification score corresponding to each label." }, "SortOrder":{ "type":"string", "enum":[ "asc", "dsc" ], "documentation":"The sort order specified in a listing condition. Possible values include the following:
asc
- Present the information in ascending order (from A-Z).dsc
- Present the information in descending order (from Z-A).String type.
" }, "TrainingParameters":{ "type":"map", "key":{"shape":"StringType"}, "value":{"shape":"StringType"} }, "UpdateBatchPredictionInput":{ "type":"structure", "required":[ "BatchPredictionId", "BatchPredictionName" ], "members":{ "BatchPredictionId":{ "shape":"EntityId", "documentation":"The ID assigned to the BatchPrediction
during creation.
A new user-supplied name or description of the BatchPrediction
.
The ID assigned to the BatchPrediction
during creation. This value should be identical to the value of the BatchPredictionId
in the request.
Represents the output of an UpdateBatchPrediction operation.
You can see the updated content by using the GetBatchPrediction operation.
" }, "UpdateDataSourceInput":{ "type":"structure", "required":[ "DataSourceId", "DataSourceName" ], "members":{ "DataSourceId":{ "shape":"EntityId", "documentation":"The ID assigned to the DataSource
during creation.
A new user-supplied name or description of the DataSource
that will replace the current description.
The ID assigned to the DataSource
during creation. This value should be identical to the value of the DataSourceID
in the request.
Represents the output of an UpdateDataSource operation.
You can see the updated content by using the GetBatchPrediction operation.
" }, "UpdateEvaluationInput":{ "type":"structure", "required":[ "EvaluationId", "EvaluationName" ], "members":{ "EvaluationId":{ "shape":"EntityId", "documentation":"The ID assigned to the Evaluation
during creation.
A new user-supplied name or description of the Evaluation
that will replace the current content.
The ID assigned to the Evaluation
during creation. This value should be identical to the value of the Evaluation
in the request.
Represents the output of an UpdateEvaluation operation.
You can see the updated content by using the GetEvaluation operation.
" }, "UpdateMLModelInput":{ "type":"structure", "required":["MLModelId"], "members":{ "MLModelId":{ "shape":"EntityId", "documentation":"The ID assigned to the MLModel
during creation.
A user-supplied name or description of the MLModel
.
The ScoreThreshold
used in binary classification MLModel
that marks the boundary between a positive prediction and a negative prediction.
Output values greater than or equal to the ScoreThreshold
receive a positive result from the MLModel
, such as true
. Output values less than the ScoreThreshold
receive a negative response from the MLModel
, such as false
.
The ID assigned to the MLModel
during creation. This value should be identical to the value of the MLModelID
in the request.
Represents the output of an UpdateMLModel operation.
You can see the updated content by using the GetMLModel operation.
" }, "VariableName":{ "type":"string", "documentation":"The name of a variable. Currently it's used to specify the name of the target value, label, weight, and tags.
" }, "VariableValue":{ "type":"string", "documentation":"The value of a variable. Currently it's used to specify values of the target value, weights, and tag variables and for filtering variable values.
" }, "Verbose":{ "type":"boolean", "documentation":"Specifies whether a describe operation should return exhaustive or abbreviated information.
" }, "VipURL":{ "type":"string", "max":2048, "pattern":"https://[a-zA-Z0-9-.]*\\.amazon(aws)?\\.com[/]?" }, "floatLabel":{"type":"float"} }, "examples":{ } }