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.
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.
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.
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.
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": "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.
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.
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.
The result of the DeleteBatchPrediction operation is irreversible.
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.
The results of the DeleteDataSource operation are irreversible.
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.
The results of the DeleteEvaluation operation are irreversible.
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.
The result of the DeleteMLModel operation is irreversible.
Deletes a real time endpoint of an MLModel.
Returns a list of BatchPrediction operations that match the search criteria in the request.
Returns a list of DataSource that match the search criteria in the request.
Returns a list of DescribeEvaluations that match the search criteria in the request.
Returns a list of MLModel that match the search criteria in the request.
Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.
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.
Returns an Evaluation that includes metadata as well as the current status of the Evaluation.
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.
Generates a prediction for the observation using the specified MLModel.
Not all response parameters will be populated because this is dependent on the type of requested model.
Updates the BatchPredictionName of a BatchPrediction.
You can use the GetBatchPrediction operation to view the contents of the updated data element.
", "UpdateDataSource": "Updates the DataSourceName of a DataSource.
You can use the GetDataSource operation to view the contents of the updated data element.
", "UpdateEvaluation": "Updates the EvaluationName of an Evaluation.
You can use the GetEvaluation operation to view the contents of the updated data element.
", "UpdateMLModel": "Updates the MLModelName and the ScoreThreshold of an MLModel.
You can use the GetMLModel operation to view the contents of the updated data element.
" }, "service": "Definition of the public APIs exposed by Amazon Machine Learning", "shapes": { "Algorithm": { "base": "The function used to train a MLModel. Training choices supported by Amazon ML include the following:
The algorithm used to train the MLModel. The following algorithm is supported:
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.
", "refs": { "BatchPrediction$CreatedByIamUser": "The AWS user account that invoked the BatchPrediction. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The AWS user account from which the DataSource was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The AWS user account that invoked the evaluation. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
", "GetBatchPredictionOutput$CreatedByIamUser": "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 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 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.
", "GetMLModelOutput$CreatedByIamUser": "The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
The AWS user account from which the MLModel was created. The account type can be either an AWS root account or an AWS Identity and Access Management (IAM) user account.
Represents the output of GetBatchPrediction operation.
The content consists of the detailed metadata, the status, and the data file information of a Batch Prediction.
", "refs": { "BatchPredictions$member": null } }, "BatchPredictionFilterVariable": { "base": "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.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.A list of BatchPrediction objects that meet the search criteria.
" } }, "ComparatorValue": { "base": "The value specified in a filtering condition. The ComparatorValue becomes the reference value when matching or evaluating data values in filtering and searching functions.
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
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
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
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
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.
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
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
The parameter is true if statistics need to be generated from the observation data.
The parameter is true if statistics need to be generated from the observation data.
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.
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.
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.
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.
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.
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.
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.
A JSON string that represents the splitting requirement of a Datasource.
A JSON string that captures the splitting rearrangement requirement of the DataSource.
DataRearrangement - A JSON string that represents the splitting requirement of a DataSource.
Sample - \"{\\\"randomSeed\\\":\\\"some-random-seed\\\", \\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
Describes the splitting specifications for a DataSource.
Describes the splitting requirement of a Datasource.
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.
The schema used by all of the data files of this DataSource.
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.
A JSON string that represents the schema. This is not required if DataSchemaUri is specified.
Describes the schema for an Amazon Redshift DataSource.
Describes the schema for an Amazon S3 DataSource.
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.
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.A list of DataSource that meet the search criteria.
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.
Represents the output of a DeleteDataSource operation.
", "refs": { } }, "DeleteEvaluationInput": { "base": null, "refs": { } }, "DeleteEvaluationOutput": { "base": "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.
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.
Represents the output of an DeleteRealtimeEndpoint operation.
The result contains the MLModelId and the endpoint information for the MLModel.
Represents the output of a DescribeBatchPredictions operation. The content is essentially a list of BatchPredictions.
Represents the query results from a DescribeDataSources operation. The content is essentially a list of DataSource.
Represents the query results from a DescribeEvaluations operation. The content is essentially a list of Evaluation.
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.",
"refs": {
"DetailsMap$key": null
}
},
"DetailsMap": {
"base": "Provides any additional details regarding the prediction.",
"refs": {
"Prediction$details": null
}
},
"DetailsValue": {
"base": null,
"refs": {
"DetailsMap$value": null
}
},
"EDPPipelineId": {
"base": null,
"refs": {
"RDSMetadata$DataPipelineId": "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.
" } }, "EDPResourceRole": { "base": null, "refs": { "RDSDataSpec$ResourceRole": "The role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (Amazon EC2) instance to carry out the copy operation from Amazon RDS to an Amazon S3 task. For more information, see Role templates for data pipelines.
", "RDSMetadata$ResourceRole": "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.
" } }, "EDPSecurityGroupId": { "base": null, "refs": { "EDPSecurityGroupIds$member": null } }, "EDPSecurityGroupIds": { "base": null, "refs": { "RDSDataSpec$SecurityGroupIds": "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.
" } }, "EDPServiceRole": { "base": null, "refs": { "RDSDataSpec$ServiceRole": "The role (DataPipelineDefaultRole) assumed by AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
", "RDSMetadata$ServiceRole": "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.
" } }, "EDPSubnetId": { "base": null, "refs": { "RDSDataSpec$SubnetId": "The subnet ID to be used to access a VPC-based RDS DB instance. This attribute is used by Data Pipeline to carry out the copy task from Amazon RDS to Amazon S3.
" } }, "EntityId": { "base": null, "refs": { "BatchPrediction$BatchPredictionId": "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.
A user-supplied ID that uniquely identifies the BatchPrediction.
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.
A user-supplied ID that uniquely identifies the BatchPrediction. This value is identical to the value of the BatchPredictionId in the request.
A user-supplied ID that uniquely identifies the DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource.
A user-supplied ID that uniquely identifies the datasource. This value should be identical to the value of the DataSourceID in the request.
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.
A user-supplied identifier 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.
A user-supplied ID that uniquely identifies 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.
A user-supplied ID that uniquely identifies the MLModel.
The DataSource that points to the training data.
A user-supplied ID that uniquely identifies the MLModel. This value should be identical to the value of the MLModelId in the request.
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 ID that is assigned to the DataSource during creation.
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.
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.
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.
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.
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 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.
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 ID assigned to the DataSource at creation.
The ID assigned to the DataSource at creation. This value should be identical to the value of the DataSourceId in the request.
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 ID assigned to the MLModel at creation.
The MLModel ID which is same as the MLModelId in the request.
The ID of the training DataSource.
The ID assigned to the MLModel at creation.
The ID of the training DataSource. The CreateMLModel operation uses the TrainingDataSourceId.
A unique identifier of the MLModel.
The ID assigned to the BatchPrediction during creation.
The ID assigned to the BatchPrediction during creation. This value should be identical to the value of the BatchPredictionId in the request.
The ID assigned to the DataSource during creation.
The ID assigned to the DataSource during creation. This value should be identical to the value of the DataSourceID in the request.
The ID assigned to the Evaluation during creation.
The ID assigned to the Evaluation during creation. This value should be identical to the value of the Evaluation in the request.
The ID assigned to the MLModel during creation.
The ID assigned to the MLModel during creation. This value should be identical to the value of the MLModelID in the request.
A user-supplied name or description of the Amazon ML resource.
", "refs": { "BatchPrediction$Name": "A user-supplied name or description of the BatchPrediction.
A user-supplied name or description of the BatchPrediction. BatchPredictionName can only use the UTF-8 character set.
A user-supplied name or description of the DataSource.
A user-supplied name or description of the DataSource.
A user-supplied name or description of the DataSource.
A user-supplied name or description of the Evaluation.
A user-supplied name or description of the MLModel.
A user-supplied name or description of the DataSource.
A user-supplied name or description of the Evaluation.
A user-supplied name or description of the BatchPrediction.
A user-supplied name or description of the DataSource.
A user-supplied name or description of the Evaluation.
A new user-supplied name or description of the BatchPrediction.
A new user-supplied name or description of the DataSource that will replace the current description.
A new user-supplied name or description of the Evaluation that will replace the current content.
A user-supplied name or description of the MLModel.
Entity status with the following possible values:
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 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.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.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 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.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.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 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.A timestamp represented in epoch time.
", "refs": { "BatchPrediction$CreatedAt": "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.
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 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.
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.
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 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.
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.
The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
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.
The time of the most recent edit to the ScoreThreshold. The time is expressed in epoch time.
The time that the request to create the real-time endpoint for the MLModel was received. The time is expressed in epoch time.
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.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.A list of Evaluation that meet the search criteria.
" } }, "GetBatchPredictionInput": { "base": null, "refs": { } }, "GetBatchPredictionOutput": { "base": "Represents the output of a GetBatchPrediction operation and describes a BatchPrediction.
Represents the output of a GetDataSource operation and describes a DataSource.
Represents the output of a GetEvaluation operation and describes an Evaluation.
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.
", "refs": { } }, "IntegerType": { "base": "Integer type that is a 32-bit signed number.
", "refs": { "RealtimeEndpointInfo$PeakRequestsPerSecond": " The maximum processing rate for the real-time endpoint for MLModel, measured in incoming requests per second.
An error on the server occurred when trying to process a request.
", "refs": { } }, "InvalidInputException": { "base": "An error on the client occurred. Typically, the cause is an invalid input value.
", "refs": { } }, "Label": { "base": null, "refs": { "Prediction$predictedLabel": "The prediction label for either a BINARY or MULTICLASSMLModel.",
"ScoreValuePerLabelMap$key": null
}
},
"LimitExceededException": {
"base": "The subscriber exceeded the maximum number of operations. This exception can occur when listing objects such as DataSource.
Long integer type that is a 64-bit signed number.
", "refs": { "DataSource$DataSizeInBytes": "The total number of observations contained in the data files that the DataSource references.
The number of data files referenced by the DataSource.
The total size of observations in the data files.
", "GetDataSourceOutput$NumberOfFiles": "The number of data files referenced by the DataSource.
Represents the output of a GetMLModel operation.
The content consists of the detailed metadata and the current status of the MLModel.
Use one of the following variables to filter a list of MLModel:
CreatedAt - Sets the search criteria to MLModel creation date.Status - Sets the search criteria to MLModel status.Name - Sets the search criteria to the contents of MLModel Name.IAMUser - Sets the search criteria to the user account that invoked the MLModel creation.TrainingDataSourceId - Sets the search criteria to the DataSource used to train one or more MLModel.RealtimeEndpointStatus - Sets the search criteria to the MLModel real-time endpoint status.MLModelType - Sets the search criteria to MLModel type: binary, regression, or multi-class.Algorithm - Sets the search criteria to the algorithm that the MLModel uses.TrainingDataURI - Sets the search criteria to the data file(s) used in training a MLModel. The URL can identify either a file or an Amazon Simple Storage Service (Amazon S3) bucket or directory.A user-supplied name or description of the MLModel.
A user-supplied name or description of the MLModel.
The category of supervised learning that this MLModel will address. Choose from the following types:
REGRESSION if the MLModel will be used to predict a numeric value.BINARY if the MLModel result has two possible values.MULTICLASS if the MLModel result has a limited number of values. For more information, see the Amazon Machine Learning Developer Guide.
", "GetMLModelOutput$MLModelType": "Identifies the MLModel category. The following are the available types:
Identifies the MLModel category. The following are the available types:
A list of MLModel that meet the search criteria.
" } }, "Message": { "base": "Description of the most recent details about an entity.
", "refs": { "BatchPrediction$Message": "A description of the most recent details about processing the batch prediction request.
", "DataSource$Message": "A description of the most recent details about creating the DataSource.
A description of the most recent details about evaluating the MLModel.
A description of the most recent details about processing the batch prediction request.
", "GetDataSourceOutput$Message": "The description of the most recent details about creating the DataSource.
A description of the most recent details about evaluating the MLModel.
Description of the most recent details about accessing the MLModel.
A description of the most recent details about accessing the MLModel.
The number of pages of information to include in the result. The range of acceptable values is 1 through 100. The default value is 100.
", "DescribeDataSourcesInput$Limit": " The maximum number of DataSource to include in the result.
The maximum number of Evaluation to include in the result.
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.
" } }, "PerformanceMetrics": { "base": "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.
", "refs": { "Evaluation$PerformanceMetrics": "Measurements of how well the MLModel performed, using observations referenced by the DataSource. One of the following metrics is returned, based on the type of the MLModel:
BinaryAUC: A binary MLModel uses the Area Under the Curve (AUC) technique to measure performance.
RegressionRMSE: A regression MLModel uses the Root Mean Square Error (RMSE) technique to measure performance. RMSE measures the difference between predicted and actual values for a single variable.
MulticlassAvgFScore: A multiclass MLModel uses the F1 score technique to measure performance.
For more information about performance metrics, please see the Amazon Machine Learning Developer Guide.
", "GetEvaluationOutput$PerformanceMetrics": "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.
" } }, "PerformanceMetricsProperties": { "base": null, "refs": { "PerformanceMetrics$Properties": null } }, "PerformanceMetricsPropertyKey": { "base": null, "refs": { "PerformanceMetricsProperties$key": null } }, "PerformanceMetricsPropertyValue": { "base": null, "refs": { "PerformanceMetricsProperties$value": null } }, "PredictInput": { "base": null, "refs": { } }, "PredictOutput": { "base": null, "refs": { } }, "Prediction": { "base": "The output from a Predict operation:
Details - Contains the following attributes: DetailsAttributes.PREDICTIVE_MODEL_TYPE - REGRESSION | BINARY | MULTICLASS DetailsAttributes.ALGORITHM - SGD
PredictedLabel - Present for either a BINARY or MULTICLASS MLModel request.
PredictedScores - Contains the raw classification score corresponding to each label.
PredictedValue - Present for a REGRESSION MLModel request.
The exception is thrown when a predict request is made to an unmounted MLModel.
A link to the file that contains logs of the CreateBatchPrediction operation.
", "GetDataSourceOutput$LogUri": "A link to the file containining logs of either create DataSource operation.
A link to the file that contains logs of the CreateEvaluation operation.
", "GetMLModelOutput$LogUri": "A link to the file that contains logs of the CreateMLModel operation.
The data specification of an Amazon Relational Database Service (Amazon RDS) DataSource.
The data specification of an Amazon RDS DataSource:
DatabaseInformation -
DatabaseName - Name of the Amazon RDS database. InstanceIdentifier - Unique identifier for the Amazon RDS database instance.DatabaseCredentials - AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon RDS database.
ResourceRole - Role (DataPipelineDefaultResourceRole) assumed by an Amazon Elastic Compute Cloud (EC2) instance to carry out the copy task from Amazon RDS to Amazon S3. For more information, see Role templates for data pipelines.
ServiceRole - Role (DataPipelineDefaultRole) assumed by the AWS Data Pipeline service to monitor the progress of the copy task from Amazon RDS to Amazon Simple Storage Service (S3). For more information, see Role templates for data pipelines.
SecurityInfo - Security information to use to access an Amazon RDS instance. You need to set up appropriate ingress rules for the security entity IDs provided to allow access to the Amazon RDS instance. Specify a [SubnetId, SecurityGroupIds] pair for a VPC-based Amazon RDS instance.
SelectSqlQuery - Query that is used to retrieve the observation data for the Datasource.
S3StagingLocation - Amazon S3 location for staging RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
DataSchemaUri - Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting requirement of a Datasource.
Sample - \"{\\\"randomSeed\\\":\\\"some-random-seed\\\", \\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
The database details of an Amazon RDS database.
", "refs": { "RDSDataSpec$DatabaseInformation": "Describes the DatabaseName and InstanceIdentifier of an an Amazon RDS database.
The database details required to connect to an Amazon RDS.
" } }, "RDSDatabaseCredentials": { "base": "The database credentials to connect to a database on an RDS DB instance.
", "refs": { "RDSDataSpec$DatabaseCredentials": "The AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon RDS database.
" } }, "RDSDatabaseName": { "base": "The name of a database hosted on an RDS DB instance.
", "refs": { "RDSDatabase$DatabaseName": null } }, "RDSDatabasePassword": { "base": "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 ID of an RDS DB instance.
" } }, "RDSMetadata": { "base": "The datasource details that are specific to Amazon RDS.
", "refs": { "DataSource$RDSMetadata": null, "GetDataSourceOutput$RDSMetadata": null } }, "RDSSelectSqlQuery": { "base": "The SQL query to be executed against the Amazon RDS database. The SQL query should be valid for the Amazon RDS type being used.
", "refs": { "RDSDataSpec$SelectSqlQuery": "The query that is used to retrieve the observation data for the DataSource.
The SQL query that is supplied during CreateDataSourceFromRDS. Returns only if Verbose is true in GetDataSourceInput.
Describes the real-time endpoint information for an MLModel.
The endpoint information of the MLModel
The endpoint information of the MLModel
The current endpoint of the MLModel
The current endpoint of the MLModel.
The current status of the real-time endpoint for the MLModel. This element can have one of the following values:
The data recipe for creating MLModel. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
The recipe to use when training the MLModel. The Recipe provides detailed information about the observation data to use during training, as well as manipulations to perform on the observation data during training.
This parameter is provided as part of the verbose format.
A map of variable name-value pairs that represent an observation.
", "refs": { "PredictInput$Record": null } }, "RedshiftClusterIdentifier": { "base": "The ID of an Amazon Redshift cluster.
", "refs": { "RedshiftDatabase$ClusterIdentifier": null } }, "RedshiftDataSpec": { "base": "Describes the data specification of an Amazon Redshift DataSource.
The data specification of an Amazon Redshift DataSource:
DatabaseInformation -
DatabaseName - Name of the Amazon Redshift database. ClusterIdentifier - Unique ID for the Amazon Redshift cluster.DatabaseCredentials - AWS Identity abd Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.
SelectSqlQuery - Query that is used to retrieve the observation data for the Datasource.
S3StagingLocation - Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Relational Database Service (Amazon RDS) using SelectSqlQuery is stored in this location.
DataSchemaUri - Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting requirement of a Datasource.
Sample - \"{\\\"randomSeed\\\":\\\"some-random-seed\\\", \\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
Describes the database details required to connect to an Amazon Redshift database.
", "refs": { "RedshiftDataSpec$DatabaseInformation": "Describes the DatabaseName and ClusterIdentifier for an Amazon Redshift DataSource.
Describes the database credentials for connecting to a database on an Amazon Redshift cluster.
", "refs": { "RedshiftDataSpec$DatabaseCredentials": "Describes AWS Identity and Access Management (IAM) credentials that are used connect to the Amazon Redshift database.
" } }, "RedshiftDatabaseName": { "base": "The name of a database hosted on an Amazon Redshift cluster.
", "refs": { "RedshiftDatabase$DatabaseName": null } }, "RedshiftDatabasePassword": { "base": "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.
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.
", "refs": { "RedshiftDataSpec$SelectSqlQuery": "Describes the SQL Query to execute on an Amazon Redshift database for an Amazon Redshift DataSource.
The SQL query that is specified during CreateDataSourceFromRedshift. Returns only if Verbose is true in GetDataSourceInput.
A specified resource cannot be located.
", "refs": { } }, "RoleARN": { "base": "The Amazon Resource Name (ARN) of an AWS IAM Role such as the following: arn:aws:iam::account:role/rolename.
", "refs": { "CreateDataSourceFromRDSInput$RoleARN": "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.
", "CreateDataSourceFromRedshiftInput$RoleARN": "
A fully specified role Amazon Resource Name (ARN). Amazon ML assumes the role on behalf of the user to create the following:
A security group to allow Amazon ML to execute the SelectSqlQuery query on an Amazon Redshift cluster
An Amazon S3 bucket policy to grant Amazon ML read/write permissions on the S3StagingLocation
Describes the data specification of a DataSource.
The data specification of a DataSource:
DataLocationS3 - Amazon Simple Storage Service (Amazon S3) location of the observation data.
DataSchemaLocationS3 - Amazon S3 location of the DataSchema.
DataSchema - A JSON string representing the schema. This is not required if DataSchemaUri is specified.
DataRearrangement - A JSON string representing the splitting requirement of a Datasource.
Sample - \"{\\\"randomSeed\\\":\\\"some-random-seed\\\", \\\"splitting\\\":{\\\"percentBegin\\\":10,\\\"percentEnd\\\":60}}\"
A reference to a file or bucket on Amazon Simple Storage Service (Amazon S3).
", "refs": { "BatchPrediction$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "BatchPrediction$OutputUri": "The location of an Amazon S3 bucket or directory to receive the operation results. The following substrings are not allowed in the s3 key portion of the \"outputURI\" field: ':', '//', '/./', '/../'.
", "CreateBatchPredictionInput$OutputUri": "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.
", "CreateMLModelInput$RecipeUri": "The Amazon Simple Storage Service (Amazon S3) location and file name that contains the MLModel recipe. You must specify either the recipe or its URI. If you don’t specify a recipe or its URI, Amazon ML creates a default.
The location and name of the data in Amazon Simple Storage Service (Amazon S3) that is used by a DataSource.
The location and name of the data in Amazon Simple Storage Server (Amazon S3) that is used in the evaluation.
", "GetBatchPredictionOutput$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "GetBatchPredictionOutput$OutputUri": "The location of an Amazon S3 bucket or directory to receive the operation results.
", "GetDataSourceOutput$DataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "GetEvaluationOutput$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "GetMLModelOutput$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "MLModel$InputDataLocationS3": "The location of the data file or directory in Amazon Simple Storage Service (Amazon S3).
", "RDSDataSpec$S3StagingLocation": "The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
The Amazon S3 location of the DataSchema.
Describes an Amazon S3 location to store the result set of the SelectSqlQuery query.
Describes the schema location for an Amazon Redshift DataSource.
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 schema Location in Amazon S3.
" } }, "ScoreThreshold": { "base": null, "refs": { "GetMLModelOutput$ScoreThreshold": "The scoring threshold is used in binary classification MLModels, and marks the boundary between a positive prediction and a negative prediction.
Output values greater than or equal to the threshold receive a positive result from the MLModel, such as true. Output values less than the threshold receive a negative response from the MLModel, such as false.
The ScoreThreshold used in binary classification MLModel that marks the boundary between a positive prediction and a negative prediction.
Output values greater than or equal to the ScoreThreshold receive a positive result from the MLModel, such as true. Output values less than the ScoreThreshold receive a negative response from the MLModel, such as false.
The sort order specified in a listing condition. Possible values include the following:
asc - Present the information in ascending order (from A-Z).dsc - Present the information in descending order (from Z-A).A two-value parameter that determines the sequence of the resulting list of MLModels.
asc - Arranges the list in ascending order (A-Z, 0-9).dsc - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.
A two-value parameter that determines the sequence of the resulting list of DataSource.
asc - Arranges the list in ascending order (A-Z, 0-9).dsc - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.
A two-value parameter that determines the sequence of the resulting list of Evaluation.
asc - Arranges the list in ascending order (A-Z, 0-9).dsc - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.
A two-value parameter that determines the sequence of the resulting list of MLModel.
asc - Arranges the list in ascending order (A-Z, 0-9).dsc - Arranges the list in descending order (Z-A, 9-0).Results are sorted by FilterVariable.
String type.
", "refs": { "DescribeBatchPredictionsInput$NextToken": "An ID of the page in the paginated results.
", "DescribeBatchPredictionsOutput$NextToken": "The ID of the next page in the paginated results that indicates at least one more page follows.
", "DescribeDataSourcesInput$NextToken": "The ID of the page in the paginated results.
", "DescribeDataSourcesOutput$NextToken": "An ID of the next page in the paginated results that indicates at least one more page follows.
", "DescribeEvaluationsInput$NextToken": "The ID of the page in the paginated results.
", "DescribeEvaluationsOutput$NextToken": "The ID of the next page in the paginated results that indicates at least one more page follows.
", "DescribeMLModelsInput$NextToken": "The ID of the page in the paginated results.
", "DescribeMLModelsOutput$NextToken": "The ID of the next page in the paginated results that indicates at least one more page follows.
", "TrainingParameters$key": null, "TrainingParameters$value": null } }, "TrainingParameters": { "base": null, "refs": { "CreateMLModelInput$Parameters": "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.
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.
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.
Represents the output of an UpdateBatchPrediction operation.
You can see the updated content by using the GetBatchPrediction operation.
", "refs": { } }, "UpdateDataSourceInput": { "base": null, "refs": { } }, "UpdateDataSourceOutput": { "base": "Represents the output of an UpdateDataSource operation.
You can see the updated content by using the GetBatchPrediction operation.
", "refs": { } }, "UpdateEvaluationInput": { "base": null, "refs": { } }, "UpdateEvaluationOutput": { "base": "Represents the output of an UpdateEvaluation operation.
You can see the updated content by using the GetEvaluation operation.
", "refs": { } }, "UpdateMLModelInput": { "base": null, "refs": { } }, "UpdateMLModelOutput": { "base": "Represents the output of an UpdateMLModel operation.
You can see the updated content by using the GetMLModel operation.
", "refs": { } }, "VariableName": { "base": "The name of a variable. Currently it's used to specify the name of the target value, label, weight, and tags.
", "refs": { "Record$key": null } }, "VariableValue": { "base": "The value of a variable. Currently it's used to specify values of the target value, weights, and tag variables and for filtering variable values.
", "refs": { "Record$value": null } }, "Verbose": { "base": "Specifies whether a describe operation should return exhaustive or abbreviated information.
", "refs": { "GetDataSourceInput$Verbose": "Specifies whether the GetDataSource operation should return DataSourceSchema.
If true, DataSourceSchema is returned.
If false, DataSourceSchema is not returned.
Specifies whether the GetMLModel operation should return Recipe.
If true, Recipe is returned.
If false, Recipe is not returned.
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.
MLModel."
}
}
}
}