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Class: Aws::MachineLearning::Client

Inherits:

Seahorse::Client::Base

Object
Seahorse::Client::Base
Aws::MachineLearning::Client

show all

Defined in:: (unknown)

Overview

An API client for Amazon Machine Learning. To construct a client, you need to configure a :region and :credentials.

machinelearning = Aws::MachineLearning::Client.new(
  region: region_name,
  credentials: credentials,
  # ...
)

See #initialize for a full list of supported configuration options.

Region

You can configure a default region in the following locations:

ENV['AWS_REGION']
Aws.config[:region]

Go here for a list of supported regions.

Credentials

Default credentials are loaded automatically from the following locations:

ENV['AWS_ACCESS_KEY_ID'] and ENV['AWS_SECRET_ACCESS_KEY']
Aws.config[:credentials]
The shared credentials ini file at ~/.aws/credentials (more information)
From an instance profile when running on EC2

You can also construct a credentials object from one of the following classes:

Alternatively, you configure credentials with :access_key_id and :secret_access_key:

# load credentials from disk
creds = YAML.load(File.read('/path/to/secrets'))

Aws::MachineLearning::Client.new(
  access_key_id: creds['access_key_id'],
  secret_access_key: creds['secret_access_key']
)

Always load your credentials from outside your application. Avoid configuring credentials statically and never commit them to source control.

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

#config, #handlers

Constructor collapse

#initialize(options = {}) ⇒ Aws::MachineLearning::Client constructor
Constructs an API client.

API Operations collapse

#add_tags(options = {}) ⇒ Types::AddTagsOutput
Adds one or more tags to an object, up to a limit of 10.
#create_batch_prediction(options = {}) ⇒ Types::CreateBatchPredictionOutput
Generates predictions for a group of observations.
#create_data_source_from_rds(options = {}) ⇒ Types::CreateDataSourceFromRDSOutput
Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS).
#create_data_source_from_redshift(options = {}) ⇒ Types::CreateDataSourceFromRedshiftOutput
Creates a DataSource from a database hosted on an Amazon Redshift cluster.
#create_data_source_from_s3(options = {}) ⇒ Types::CreateDataSourceFromS3Output
Creates a DataSource object.
#create_evaluation(options = {}) ⇒ Types::CreateEvaluationOutput
Creates a new Evaluation of an MLModel.
#create_ml_model(options = {}) ⇒ Types::CreateMLModelOutput
Creates a new MLModel using the DataSource and the recipe as information sources.
#create_realtime_endpoint(options = {}) ⇒ Types::CreateRealtimeEndpointOutput
Creates a real-time endpoint for the MLModel.
#delete_batch_prediction(options = {}) ⇒ Types::DeleteBatchPredictionOutput
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.
.
#delete_data_source(options = {}) ⇒ Types::DeleteDataSourceOutput
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.
.
#delete_evaluation(options = {}) ⇒ Types::DeleteEvaluationOutput
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.
.
#delete_ml_model(options = {}) ⇒ Types::DeleteMLModelOutput
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.
.
#delete_realtime_endpoint(options = {}) ⇒ Types::DeleteRealtimeEndpointOutput
Deletes a real time endpoint of an MLModel.

.
#delete_tags(options = {}) ⇒ Types::DeleteTagsOutput
Deletes the specified tags associated with an ML object.
#describe_batch_predictions(options = {}) ⇒ Types::DescribeBatchPredictionsOutput
Returns a list of BatchPrediction operations that match the search criteria in the request.

.
#describe_data_sources(options = {}) ⇒ Types::DescribeDataSourcesOutput
Returns a list of DataSource that match the search criteria in the request.

.
#describe_evaluations(options = {}) ⇒ Types::DescribeEvaluationsOutput
Returns a list of DescribeEvaluations that match the search criteria in the request.

.
#describe_ml_models(options = {}) ⇒ Types::DescribeMLModelsOutput
Returns a list of MLModel that match the search criteria in the request.

.
#describe_tags(options = {}) ⇒ Types::DescribeTagsOutput
Describes one or more of the tags for your Amazon ML object.

.
#get_batch_prediction(options = {}) ⇒ Types::GetBatchPredictionOutput
Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

.
#get_data_source(options = {}) ⇒ Types::GetDataSourceOutput
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.
#get_evaluation(options = {}) ⇒ Types::GetEvaluationOutput
Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

.
#get_ml_model(options = {}) ⇒ Types::GetMLModelOutput
Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

GetMLModel provides results in normal or verbose format.
#predict(options = {}) ⇒ Types::PredictOutput
Generates a prediction for the observation using the specified ML Model.

Note
Not all response parameters will be populated.
#update_batch_prediction(options = {}) ⇒ Types::UpdateBatchPredictionOutput
Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.
.
#update_data_source(options = {}) ⇒ Types::UpdateDataSourceOutput
Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.
.
#update_evaluation(options = {}) ⇒ Types::UpdateEvaluationOutput
Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.
.
#update_ml_model(options = {}) ⇒ Types::UpdateMLModelOutput
Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.
.

Instance Method Summary collapse

#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ Boolean
Waiters polls an API operation until a resource enters a desired state.
#waiter_names ⇒ Array<Symbol>
Returns the list of supported waiters.

Methods inherited from Seahorse::Client::Base

add_plugin, api, #build_request, clear_plugins, define, new, #operation, #operation_names, plugins, remove_plugin, set_api, set_plugins

Methods included from Seahorse::Client::HandlerBuilder

#handle, #handle_request, #handle_response

Constructor Details

#initialize(options = {}) ⇒ `Aws::MachineLearning::Client`

Constructs an API client.

Options Hash (options):

:access_key_id (String) —
Used to set credentials statically. See Plugins::RequestSigner for more details.
:active_endpoint_cache (Boolean) —
When set to true, a thread polling for endpoints will be running in the background every 60 secs (default). Defaults to false. See Plugins::EndpointDiscovery for more details.
:convert_params (Boolean) — default: true —
When true, an attempt is made to coerce request parameters into the required types. See Plugins::ParamConverter for more details.
:credentials (required, Credentials) —
Your AWS credentials. The following locations will be searched in order for credentials:
- :access_key_id, :secret_access_key, and :session_token options
- ENV['AWS_ACCESS_KEY_ID'], ENV['AWS_SECRET_ACCESS_KEY']
- HOME/.aws/credentials shared credentials file
- EC2 instance profile credentials See Plugins::RequestSigner for more details.
:disable_host_prefix_injection (Boolean) —
Set to true to disable SDK automatically adding host prefix to default service endpoint when available. See Plugins::EndpointPattern for more details.
:endpoint (String) —
A default endpoint is constructed from the :region. See Plugins::RegionalEndpoint for more details.
:endpoint_cache_max_entries (Integer) —
Used for the maximum size limit of the LRU cache storing endpoints data for endpoint discovery enabled operations. Defaults to 1000. See Plugins::EndpointDiscovery for more details.
:endpoint_cache_max_threads (Integer) —
Used for the maximum threads in use for polling endpoints to be cached, defaults to 10. See Plugins::EndpointDiscovery for more details.
:endpoint_cache_poll_interval (Integer) —
When :endpoint_discovery and :active_endpoint_cache is enabled, Use this option to config the time interval in seconds for making requests fetching endpoints information. Defaults to 60 sec. See Plugins::EndpointDiscovery for more details.
:endpoint_discovery (Boolean) —
When set to true, endpoint discovery will be enabled for operations when available. Defaults to false. See Plugins::EndpointDiscovery for more details.
:http_continue_timeout (Float) — default: 1 —
See Seahorse::Client::Plugins::NetHttp for more details.
:http_idle_timeout (Integer) — default: 5 —
See Seahorse::Client::Plugins::NetHttp for more details.
:http_open_timeout (Integer) — default: 15 —
See Seahorse::Client::Plugins::NetHttp for more details.
:http_proxy (String) —
See Seahorse::Client::Plugins::NetHttp for more details.
:http_read_timeout (Integer) — default: 60 —
See Seahorse::Client::Plugins::NetHttp for more details.
:http_wire_trace (Boolean) — default: false —
See Seahorse::Client::Plugins::NetHttp for more details.
:log_level (Symbol) — default: :info —
The log level to send messages to the logger at. See Plugins::Logging for more details.
:log_formatter (Logging::LogFormatter) —
The log formatter. Defaults to Seahorse::Client::Logging::Formatter.default. See Plugins::Logging for more details.
:logger (Logger) — default: nil —
The Logger instance to send log messages to. If this option is not set, logging will be disabled. See Plugins::Logging for more details.
:profile (String) —
Used when loading credentials from the shared credentials file at HOME/.aws/credentials. When not specified, 'default' is used. See Plugins::RequestSigner for more details.
:raise_response_errors (Boolean) — default: true —
When true, response errors are raised. See Seahorse::Client::Plugins::RaiseResponseErrors for more details.
:region (required, String) —
The AWS region to connect to. The region is used to construct the client endpoint. Defaults to ENV['AWS_REGION']. Also checks AMAZON_REGION and AWS_DEFAULT_REGION. See Plugins::RegionalEndpoint for more details.
:retry_limit (Integer) — default: 3 —
The maximum number of times to retry failed requests. Only ~ 500 level server errors and certain ~ 400 level client errors are retried. Generally, these are throttling errors, data checksum errors, networking errors, timeout errors and auth errors from expired credentials. See Plugins::RetryErrors for more details.
:secret_access_key (String) —
Used to set credentials statically. See Plugins::RequestSigner for more details.
:session_token (String) —
Used to set credentials statically. See Plugins::RequestSigner for more details.
:simple_json (Boolean) — default: false —
Disables request parameter conversion, validation, and formatting. Also disable response data type conversions. This option is useful when you want to ensure the highest level of performance by avoiding overhead of walking request parameters and response data structures.

When :simple_json is enabled, the request parameters hash must be formatted exactly as the DynamoDB API expects. See Plugins::Protocols::JsonRpc for more details.
:ssl_ca_bundle (String) —
See Seahorse::Client::Plugins::NetHttp for more details.
:ssl_ca_directory (String) —
See Seahorse::Client::Plugins::NetHttp for more details.
:ssl_ca_store (String) —
See Seahorse::Client::Plugins::NetHttp for more details.
:ssl_verify_peer (Boolean) — default: true —
See Seahorse::Client::Plugins::NetHttp for more details.
:stub_responses (Boolean) — default: false —
Causes the client to return stubbed responses. By default fake responses are generated and returned. You can specify the response data to return or errors to raise by calling ClientStubs#stub_responses. See ClientStubs for more information.

Please note When response stubbing is enabled, no HTTP requests are made, and retries are disabled. See Plugins::StubResponses for more details.
:validate_params (Boolean) — default: true —
When true, request parameters are validated before sending the request. See Plugins::ParamValidator for more details.

Instance Method Details

#add_tags(options = {}) ⇒ `Types::AddTagsOutput`

Adds one or more tags to an object, up to a limit of 10. Each tag consists of a key and an optional value. If you add a tag using a key that is already associated with the ML object, AddTags updates the tag's value.

Examples:

Request syntax with placeholder values


resp = client.add_tags({
  tags: [ # required
    {
      key: "TagKey",
      value: "TagValue",
    },
  ],
  resource_id: "EntityId", # required
  resource_type: "BatchPrediction", # required, accepts BatchPrediction, DataSource, Evaluation, MLModel
})

Response structure


resp.resource_id #=> String
resp.resource_type #=> String, one of "BatchPrediction", "DataSource", "Evaluation", "MLModel"

Options Hash (options):

:tags (required, Array<Types::Tag>) —
The key-value pairs to use to create tags. If you specify a key without specifying a value, Amazon ML creates a tag with the specified key and a value of null.
:resource_id (required, String) —
The ID of the ML object to tag. For example, exampleModelId.
:resource_type (required, String) —
The type of the ML object to tag.

Returns:

(Types::AddTagsOutput) —
Returns a response object which responds to the following methods:
- #resource_id => String
- #resource_type => String

#create_batch_prediction(options = {}) ⇒ `Types::CreateBatchPredictionOutput`

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.

Examples:

Request syntax with placeholder values


resp = client.create_batch_prediction({
  batch_prediction_id: "EntityId", # required
  batch_prediction_name: "EntityName",
  ml_model_id: "EntityId", # required
  batch_prediction_data_source_id: "EntityId", # required
  output_uri: "S3Url", # required
})

Response structure


resp.batch_prediction_id #=> String

Options Hash (options):

:batch_prediction_id (required, String) —
A user-supplied ID that uniquely identifies the BatchPrediction.
:batch_prediction_name (String) —
A user-supplied name or description of the BatchPrediction. BatchPredictionName can only use the UTF-8 character set.
:ml_model_id (required, String) —
The ID of the MLModel that will generate predictions for the group of observations.
:batch_prediction_data_source_id (required, String) —
The ID of the DataSource that points to the group of observations to predict.
:output_uri (required, String) —
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.

Returns:

(Types::CreateBatchPredictionOutput) —
Returns a response object which responds to the following methods:
- #batch_prediction_id => String

#create_data_source_from_rds(options = {}) ⇒ `Types::CreateDataSourceFromRDSOutput`

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 the COMPLETED or PENDING state can be used only 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.

Examples:

Request syntax with placeholder values


resp = client.create_data_source_from_rds({
  data_source_id: "EntityId", # required
  data_source_name: "EntityName",
  rds_data: { # required
    database_information: { # required
      instance_identifier: "RDSInstanceIdentifier", # required
      database_name: "RDSDatabaseName", # required
    },
    select_sql_query: "RDSSelectSqlQuery", # required
    database_credentials: { # required
      username: "RDSDatabaseUsername", # required
      password: "RDSDatabasePassword", # required
    },
    s3_staging_location: "S3Url", # required
    data_rearrangement: "DataRearrangement",
    data_schema: "DataSchema",
    data_schema_uri: "S3Url",
    resource_role: "EDPResourceRole", # required
    service_role: "EDPServiceRole", # required
    subnet_id: "EDPSubnetId", # required
    security_group_ids: ["EDPSecurityGroupId"], # required
  },
  role_arn: "RoleARN", # required
  compute_statistics: false,
})

Response structure


resp.data_source_id #=> String

Options Hash (options):

:data_source_id (required, String) —
A user-supplied ID that uniquely identifies the DataSource. Typically, an Amazon Resource Number (ARN) becomes the ID for a DataSource.
:data_source_name (String) —
A user-supplied name or description of the DataSource.
:rds_data (required, Types::RDSDataSpec) —
The data specification of an Amazon RDS DataSource:
- DatabaseInformation - * DatabaseName - The name of the Amazon RDS database.
  - InstanceIdentifier - A 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 - A role (DataPipelineDefaultResourceRole) assumed by an EC2 instance to carry out the copy task from Amazon RDS to Amazon Simple Storage Service (Amazon S3). For more information, see Role templates for data pipelines.
- ServiceRole - A role (DataPipelineDefaultRole) assumed by the 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.
- SecurityInfo - The security information to use to access an RDS DB 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 RDS DB instance.
- SelectSqlQuery - A query that is used to retrieve the observation data for the Datasource.
- S3StagingLocation - The Amazon S3 location for staging Amazon RDS data. The data retrieved from Amazon RDS using SelectSqlQuery is stored in this location.
- DataSchemaUri - The 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 that represents the splitting and rearrangement requirements for the Datasource.
  
  Sample - "`{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}`"
:role_arn (required, String) —
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.
:compute_statistics (Boolean) —
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.

Returns:

(Types::CreateDataSourceFromRDSOutput) —
Returns a response object which responds to the following methods:
- #data_source_id => String

#create_data_source_from_redshift(options = {}) ⇒ `Types::CreateDataSourceFromRedshiftOutput`

Creates a DataSource from a database hosted on an Amazon Redshift cluster. 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 states can be used to perform only CreateMLModel, CreateEvaluation, or CreateBatchPrediction operations.

If Amazon ML can't 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 be contained in the database hosted on an Amazon Redshift cluster and should be specified by a SelectSqlQuery query. Amazon ML executes an Unload command in Amazon Redshift to transfer the result set of the SelectSqlQuery query to S3StagingLocation.

After the DataSource has been created, it's ready for use in evaluations and batch predictions. If you plan to use the DataSource to train an MLModel, the DataSource also requires a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

<?oxy_insert_start author="laurama" timestamp="20160406T153842-0700">

You can't change an existing datasource, but you can copy and modify the settings from an existing Amazon Redshift datasource to create a new datasource. To do so, call GetDataSource for an existing datasource and copy the values to a CreateDataSource call. Change the settings that you want to change and make sure that all required fields have the appropriate values.

<?oxy_insert_end>

Examples:

Request syntax with placeholder values


resp = client.create_data_source_from_redshift({
  data_source_id: "EntityId", # required
  data_source_name: "EntityName",
  data_spec: { # required
    database_information: { # required
      database_name: "RedshiftDatabaseName", # required
      cluster_identifier: "RedshiftClusterIdentifier", # required
    },
    select_sql_query: "RedshiftSelectSqlQuery", # required
    database_credentials: { # required
      username: "RedshiftDatabaseUsername", # required
      password: "RedshiftDatabasePassword", # required
    },
    s3_staging_location: "S3Url", # required
    data_rearrangement: "DataRearrangement",
    data_schema: "DataSchema",
    data_schema_uri: "S3Url",
  },
  role_arn: "RoleARN", # required
  compute_statistics: false,
})

Response structure


resp.data_source_id #=> String

Options Hash (options):

:data_source_id (required, String) —
A user-supplied ID that uniquely identifies the DataSource.
:data_source_name (String) —
A user-supplied name or description of the DataSource.
:data_spec (required, Types::RedshiftDataSpec) —
The data specification of an Amazon Redshift DataSource:
- DatabaseInformation - * DatabaseName - The name of the Amazon Redshift database.
  - ClusterIdentifier - The unique ID for the Amazon Redshift cluster.
- DatabaseCredentials - The AWS Identity and Access Management (IAM) credentials that are used to connect to the Amazon Redshift database.
- SelectSqlQuery - The query that is used to retrieve the observation data for the Datasource.
- S3StagingLocation - The Amazon Simple Storage Service (Amazon S3) location for staging Amazon Redshift data. The data retrieved from Amazon Redshift using the SelectSqlQuery query is stored in this location.
- DataSchemaUri - The 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 that represents the splitting and rearrangement requirements for the DataSource.
  
  Sample - "`{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}`"
:role_arn (required, String) —
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
:compute_statistics (Boolean) —
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.

Returns:

(Types::CreateDataSourceFromRedshiftOutput) —
Returns a response object which responds to the following methods:
- #data_source_id => String

#create_data_source_from_s3(options = {}) ⇒ `Types::CreateDataSourceFromS3Output`

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 has been created and is ready for use, Amazon ML sets the Status parameter to COMPLETED. DataSource in the COMPLETED or PENDING state can be used to perform only CreateMLModel, CreateEvaluation or CreateBatchPrediction operations.

If Amazon ML can't 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) location, 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 also needs a recipe. A recipe describes how each input variable will be used in training an MLModel. Will the variable be included or excluded from training? Will the variable be manipulated; for example, will it be combined with another variable or will it be split apart into word combinations? The recipe provides answers to these questions.

Examples:

Request syntax with placeholder values


resp = client.create_data_source_from_s3({
  data_source_id: "EntityId", # required
  data_source_name: "EntityName",
  data_spec: { # required
    data_location_s3: "S3Url", # required
    data_rearrangement: "DataRearrangement",
    data_schema: "DataSchema",
    data_schema_location_s3: "S3Url",
  },
  compute_statistics: false,
})

Response structure


resp.data_source_id #=> String

Options Hash (options):

:data_source_id (required, String) —
A user-supplied identifier that uniquely identifies the DataSource.
:data_source_name (String) —
A user-supplied name or description of the DataSource.
:data_spec (required, Types::S3DataSpec) —
The data specification of a DataSource:
- DataLocationS3 - The Amazon S3 location of the observation data.
- DataSchemaLocationS3 - The 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 that represents the splitting and rearrangement requirements for the Datasource.
  
  Sample - "`{\"splitting\":{\"percentBegin\":10,\"percentEnd\":60}}`"
:compute_statistics (Boolean) —
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.

Returns:

(Types::CreateDataSourceFromS3Output) —
Returns a response object which responds to the following methods:
- #data_source_id => String

#create_evaluation(options = {}) ⇒ `Types::CreateEvaluationOutput`

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.

Examples:

Request syntax with placeholder values


resp = client.create_evaluation({
  evaluation_id: "EntityId", # required
  evaluation_name: "EntityName",
  ml_model_id: "EntityId", # required
  evaluation_data_source_id: "EntityId", # required
})

Response structure


resp.evaluation_id #=> String

Options Hash (options):

:evaluation_id (required, String) —
A user-supplied ID that uniquely identifies the Evaluation.
:evaluation_name (String) —
A user-supplied name or description of the Evaluation.
:ml_model_id (required, String) —
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.
:evaluation_data_source_id (required, String) —
The ID of the DataSource for the evaluation. The schema of the DataSource must match the schema used to create the MLModel.

Returns:

(Types::CreateEvaluationOutput) —
Returns a response object which responds to the following methods:
- #evaluation_id => String

#create_ml_model(options = {}) ⇒ `Types::CreateMLModelOutput`

Creates a new MLModel using the DataSource and the recipe as information sources.

An MLModel is nearly immutable. Users can update only 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 has been created and ready is for use, Amazon ML sets the status to COMPLETED.

You can use the GetMLModel operation to check the 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.

Examples:

Request syntax with placeholder values


resp = client.create_ml_model({
  ml_model_id: "EntityId", # required
  ml_model_name: "EntityName",
  ml_model_type: "REGRESSION", # required, accepts REGRESSION, BINARY, MULTICLASS
  parameters: {
    "StringType" => "StringType",
  },
  training_data_source_id: "EntityId", # required
  recipe: "Recipe",
  recipe_uri: "S3Url",
})

Response structure


resp.ml_model_id #=> String

Options Hash (options):

:ml_model_id (required, String) —
A user-supplied ID that uniquely identifies the MLModel.
:ml_model_name (String) —
A user-supplied name or description of the MLModel.
:ml_model_type (required, String) —
The category of supervised learning that this MLModel will address. Choose from the following types:
- Choose REGRESSION if the MLModel will be used to predict a numeric value.
- Choose BINARY if the MLModel result has two possible values.
- Choose MULTICLASS if the MLModel result has a limited number of values.
For more information, see the Amazon Machine Learning Developer Guide.
:parameters (Hash<String,String>) —
A list of the training parameters in the MLModel. The list is implemented as a map of key-value pairs.

The following is the current set of training parameters:
- sgd.maxMLModelSizeInBytes - The maximum allowed size of the model. Depending on the input data, the size of the model might affect its performance.
  
  The value is an integer that ranges from 100000 to 2147483648. The default value is 33554432.
- sgd.maxPasses - The number of times that the training process traverses the observations to build the MLModel. The value is an integer that ranges from 1 to 10000. The default value is 10.
- sgd.shuffleType - Whether Amazon ML shuffles the training data. Shuffling the data improves a model\'s ability to find the optimal solution for a variety of data types. The valid values are auto and none. The default value is none. We <?oxy_insert_start author=\"laurama\" timestamp=\"20160329T131121-0700\">strongly recommend that you shuffle your data.<?oxy_insert_end>
- sgd.l1RegularizationAmount - The coefficient regularization L1 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to zero, resulting in a sparse feature set. If you use this parameter, start by specifying a small value, such as 1.0E-08.
  
  The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L1 normalization. This parameter can\'t be used when L2 is specified. Use this parameter sparingly.
- sgd.l2RegularizationAmount - The coefficient regularization L2 norm. It controls overfitting the data by penalizing large coefficients. This tends to drive coefficients to small, nonzero values. If you use this parameter, start by specifying a small value, such as 1.0E-08.
  
  The value is a double that ranges from 0 to MAX_DOUBLE. The default is to not use L2 normalization. This parameter can\'t be used when L1 is specified. Use this parameter sparingly.
:training_data_source_id (required, String) —
The DataSource that points to the training data.
:recipe (String) —
The data recipe for creating the 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.
:recipe_uri (String) —
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.

Returns:

(Types::CreateMLModelOutput) —
Returns a response object which responds to the following methods:
- #ml_model_id => String

#create_realtime_endpoint(options = {}) ⇒ `Types::CreateRealtimeEndpointOutput`

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.

Examples:

Request syntax with placeholder values


resp = client.create_realtime_endpoint({
  ml_model_id: "EntityId", # required
})

Response structure


resp.ml_model_id #=> String
resp.realtime_endpoint_info.peak_requests_per_second #=> Integer
resp.realtime_endpoint_info.created_at #=> Time
resp.realtime_endpoint_info.endpoint_url #=> String
resp.realtime_endpoint_info.endpoint_status #=> String, one of "NONE", "READY", "UPDATING", "FAILED"

Options Hash (options):

:ml_model_id (required, String) —
The ID assigned to the MLModel during creation.

Returns:

(Types::CreateRealtimeEndpointOutput) —
Returns a response object which responds to the following methods:
- #ml_model_id => String
- #realtime_endpoint_info => Types::RealtimeEndpointInfo

#delete_batch_prediction(options = {}) ⇒ `Types::DeleteBatchPredictionOutput`

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.

Examples:

Request syntax with placeholder values


resp = client.delete_batch_prediction({
  batch_prediction_id: "EntityId", # required
})

Response structure


resp.batch_prediction_id #=> String

Options Hash (options):

:batch_prediction_id (required, String) —
A user-supplied ID that uniquely identifies the BatchPrediction.

Returns:

(Types::DeleteBatchPredictionOutput) —
Returns a response object which responds to the following methods:
- #batch_prediction_id => String

#delete_data_source(options = {}) ⇒ `Types::DeleteDataSourceOutput`

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.

Examples:

Request syntax with placeholder values


resp = client.delete_data_source({
  data_source_id: "EntityId", # required
})

Response structure


resp.data_source_id #=> String

Options Hash (options):

:data_source_id (required, String) —
A user-supplied ID that uniquely identifies the DataSource.

Returns:

(Types::DeleteDataSourceOutput) —
Returns a response object which responds to the following methods:
- #data_source_id => String

#delete_evaluation(options = {}) ⇒ `Types::DeleteEvaluationOutput`

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.

Examples:

Request syntax with placeholder values


resp = client.delete_evaluation({
  evaluation_id: "EntityId", # required
})

Response structure


resp.evaluation_id #=> String

Options Hash (options):

:evaluation_id (required, String) —
A user-supplied ID that uniquely identifies the Evaluation to delete.

Returns:

(Types::DeleteEvaluationOutput) —
Returns a response object which responds to the following methods:
- #evaluation_id => String

#delete_ml_model(options = {}) ⇒ `Types::DeleteMLModelOutput`

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.

Examples:

Request syntax with placeholder values


resp = client.delete_ml_model({
  ml_model_id: "EntityId", # required
})

Response structure


resp.ml_model_id #=> String

Options Hash (options):

:ml_model_id (required, String) —
A user-supplied ID that uniquely identifies the MLModel.

Returns:

(Types::DeleteMLModelOutput) —
Returns a response object which responds to the following methods:
- #ml_model_id => String

#delete_realtime_endpoint(options = {}) ⇒ `Types::DeleteRealtimeEndpointOutput`

Deletes a real time endpoint of an MLModel.

Examples:

Request syntax with placeholder values


resp = client.delete_realtime_endpoint({
  ml_model_id: "EntityId", # required
})

Response structure


resp.ml_model_id #=> String
resp.realtime_endpoint_info.peak_requests_per_second #=> Integer
resp.realtime_endpoint_info.created_at #=> Time
resp.realtime_endpoint_info.endpoint_url #=> String
resp.realtime_endpoint_info.endpoint_status #=> String, one of "NONE", "READY", "UPDATING", "FAILED"

Options Hash (options):

:ml_model_id (required, String) —
The ID assigned to the MLModel during creation.

Returns:

(Types::DeleteRealtimeEndpointOutput) —
Returns a response object which responds to the following methods:
- #ml_model_id => String
- #realtime_endpoint_info => Types::RealtimeEndpointInfo

#delete_tags(options = {}) ⇒ `Types::DeleteTagsOutput`

Deletes the specified tags associated with an ML object. After this operation is complete, you can't recover deleted tags.

If you specify a tag that doesn't exist, Amazon ML ignores it.

Examples:

Request syntax with placeholder values


resp = client.delete_tags({
  tag_keys: ["TagKey"], # required
  resource_id: "EntityId", # required
  resource_type: "BatchPrediction", # required, accepts BatchPrediction, DataSource, Evaluation, MLModel
})

Response structure


resp.resource_id #=> String
resp.resource_type #=> String, one of "BatchPrediction", "DataSource", "Evaluation", "MLModel"

Options Hash (options):

:tag_keys (required, Array<String>) —
One or more tags to delete.
:resource_id (required, String) —
The ID of the tagged ML object. For example, exampleModelId.
:resource_type (required, String) —
The type of the tagged ML object.

Returns:

(Types::DeleteTagsOutput) —
Returns a response object which responds to the following methods:
- #resource_id => String
- #resource_type => String

#describe_batch_predictions(options = {}) ⇒ `Types::DescribeBatchPredictionsOutput`

Returns a list of BatchPrediction operations that match the search criteria in the request.

Examples:

Request syntax with placeholder values


resp = client.describe_batch_predictions({
  filter_variable: "CreatedAt", # accepts CreatedAt, LastUpdatedAt, Status, Name, IAMUser, MLModelId, DataSourceId, DataURI
  eq: "ComparatorValue",
  gt: "ComparatorValue",
  lt: "ComparatorValue",
  ge: "ComparatorValue",
  le: "ComparatorValue",
  ne: "ComparatorValue",
  prefix: "ComparatorValue",
  sort_order: "asc", # accepts asc, dsc
  next_token: "StringType",
  limit: 1,
})

Response structure


resp.results #=> Array
resp.results[0].batch_prediction_id #=> String
resp.results[0].ml_model_id #=> String
resp.results[0].batch_prediction_data_source_id #=> String
resp.results[0].input_data_location_s3 #=> String
resp.results[0].created_by_iam_user #=> String
resp.results[0].created_at #=> Time
resp.results[0].last_updated_at #=> Time
resp.results[0].name #=> String
resp.results[0].status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED"
resp.results[0].output_uri #=> String
resp.results[0].message #=> String
resp.results[0].compute_time #=> Integer
resp.results[0].finished_at #=> Time
resp.results[0].started_at #=> Time
resp.results[0].total_record_count #=> Integer
resp.results[0].invalid_record_count #=> Integer
resp.next_token #=> String

Options Hash (options):

:filter_variable (String) —
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.
:eq (String) —
The equal to operator. The BatchPrediction results will have FilterVariable values that exactly match the value specified with EQ.
:gt (String) —
The greater than operator. The BatchPrediction results will have FilterVariable values that are greater than the value specified with GT.
:lt (String) —
The less than operator. The BatchPrediction results will have FilterVariable values that are less than the value specified with LT.
:ge (String) —
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.
:le (String) —
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.
:ne (String) —
The not equal to operator. The BatchPrediction results will have FilterVariable values not equal to the value specified with NE.
:prefix (String) —
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
:sort_order (String) —
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.
:next_token (String) —
An ID of the page in the paginated results.
:limit (Integer) —
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.

Returns:

(Types::DescribeBatchPredictionsOutput) —
Returns a response object which responds to the following methods:
- #results => Array<Types::BatchPrediction>
- #next_token => String

#describe_data_sources(options = {}) ⇒ `Types::DescribeDataSourcesOutput`

Returns a list of DataSource that match the search criteria in the request.

Examples:

Request syntax with placeholder values


resp = client.describe_data_sources({
  filter_variable: "CreatedAt", # accepts CreatedAt, LastUpdatedAt, Status, Name, DataLocationS3, IAMUser
  eq: "ComparatorValue",
  gt: "ComparatorValue",
  lt: "ComparatorValue",
  ge: "ComparatorValue",
  le: "ComparatorValue",
  ne: "ComparatorValue",
  prefix: "ComparatorValue",
  sort_order: "asc", # accepts asc, dsc
  next_token: "StringType",
  limit: 1,
})

Response structure


resp.results #=> Array
resp.results[0].data_source_id #=> String
resp.results[0].data_location_s3 #=> String
resp.results[0].data_rearrangement #=> String
resp.results[0].created_by_iam_user #=> String
resp.results[0].created_at #=> Time
resp.results[0].last_updated_at #=> Time
resp.results[0].data_size_in_bytes #=> Integer
resp.results[0].number_of_files #=> Integer
resp.results[0].name #=> String
resp.results[0].status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED"
resp.results[0].message #=> String
resp.results[0].redshift_metadata.redshift_database.database_name #=> String
resp.results[0].redshift_metadata.redshift_database.cluster_identifier #=> String
resp.results[0].redshift_metadata.database_user_name #=> String
resp.results[0].redshift_metadata.select_sql_query #=> String
resp.results[0].rds_metadata.database.instance_identifier #=> String
resp.results[0].rds_metadata.database.database_name #=> String
resp.results[0].rds_metadata.database_user_name #=> String
resp.results[0].rds_metadata.select_sql_query #=> String
resp.results[0].rds_metadata.resource_role #=> String
resp.results[0].rds_metadata.service_role #=> String
resp.results[0].rds_metadata.data_pipeline_id #=> String
resp.results[0].role_arn #=> String
resp.results[0].compute_statistics #=> true/false
resp.results[0].compute_time #=> Integer
resp.results[0].finished_at #=> Time
resp.results[0].started_at #=> Time
resp.next_token #=> String

Options Hash (options):

:filter_variable (String) —
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.
:eq (String) —
The equal to operator. The DataSource results will have FilterVariable values that exactly match the value specified with EQ.
:gt (String) —
The greater than operator. The DataSource results will have FilterVariable values that are greater than the value specified with GT.
:lt (String) —
The less than operator. The DataSource results will have FilterVariable values that are less than the value specified with LT.
:ge (String) —
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.
:le (String) —
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.
:ne (String) —
The not equal to operator. The DataSource results will have FilterVariable values not equal to the value specified with NE.
:prefix (String) —
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
:sort_order (String) —
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.
:next_token (String) —
The ID of the page in the paginated results.
:limit (Integer) —
The maximum number of DataSource to include in the result.

Returns:

(Types::DescribeDataSourcesOutput) —
Returns a response object which responds to the following methods:
- #results => Array<Types::DataSource>
- #next_token => String

#describe_evaluations(options = {}) ⇒ `Types::DescribeEvaluationsOutput`

Returns a list of DescribeEvaluations that match the search criteria in the request.

Examples:

Request syntax with placeholder values


resp = client.describe_evaluations({
  filter_variable: "CreatedAt", # accepts CreatedAt, LastUpdatedAt, Status, Name, IAMUser, MLModelId, DataSourceId, DataURI
  eq: "ComparatorValue",
  gt: "ComparatorValue",
  lt: "ComparatorValue",
  ge: "ComparatorValue",
  le: "ComparatorValue",
  ne: "ComparatorValue",
  prefix: "ComparatorValue",
  sort_order: "asc", # accepts asc, dsc
  next_token: "StringType",
  limit: 1,
})

Response structure


resp.results #=> Array
resp.results[0].evaluation_id #=> String
resp.results[0].ml_model_id #=> String
resp.results[0].evaluation_data_source_id #=> String
resp.results[0].input_data_location_s3 #=> String
resp.results[0].created_by_iam_user #=> String
resp.results[0].created_at #=> Time
resp.results[0].last_updated_at #=> Time
resp.results[0].name #=> String
resp.results[0].status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED"
resp.results[0].performance_metrics.properties #=> Hash
resp.results[0].performance_metrics.properties["PerformanceMetricsPropertyKey"] #=> String
resp.results[0].message #=> String
resp.results[0].compute_time #=> Integer
resp.results[0].finished_at #=> Time
resp.results[0].started_at #=> Time
resp.next_token #=> String

Options Hash (options):

:filter_variable (String) —
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.
:eq (String) —
The equal to operator. The Evaluation results will have FilterVariable values that exactly match the value specified with EQ.
:gt (String) —
The greater than operator. The Evaluation results will have FilterVariable values that are greater than the value specified with GT.
:lt (String) —
The less than operator. The Evaluation results will have FilterVariable values that are less than the value specified with LT.
:ge (String) —
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.
:le (String) —
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.
:ne (String) —
The not equal to operator. The Evaluation results will have FilterVariable values not equal to the value specified with NE.
:prefix (String) —
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
:sort_order (String) —
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.
:next_token (String) —
The ID of the page in the paginated results.
:limit (Integer) —
The maximum number of Evaluation to include in the result.

Returns:

(Types::DescribeEvaluationsOutput) —
Returns a response object which responds to the following methods:
- #results => Array<Types::Evaluation>
- #next_token => String

#describe_ml_models(options = {}) ⇒ `Types::DescribeMLModelsOutput`

Returns a list of MLModel that match the search criteria in the request.

Examples:

Request syntax with placeholder values


resp = client.describe_ml_models({
  filter_variable: "CreatedAt", # accepts CreatedAt, LastUpdatedAt, Status, Name, IAMUser, TrainingDataSourceId, RealtimeEndpointStatus, MLModelType, Algorithm, TrainingDataURI
  eq: "ComparatorValue",
  gt: "ComparatorValue",
  lt: "ComparatorValue",
  ge: "ComparatorValue",
  le: "ComparatorValue",
  ne: "ComparatorValue",
  prefix: "ComparatorValue",
  sort_order: "asc", # accepts asc, dsc
  next_token: "StringType",
  limit: 1,
})

Response structure


resp.results #=> Array
resp.results[0].ml_model_id #=> String
resp.results[0].training_data_source_id #=> String
resp.results[0].created_by_iam_user #=> String
resp.results[0].created_at #=> Time
resp.results[0].last_updated_at #=> Time
resp.results[0].name #=> String
resp.results[0].status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED"
resp.results[0].size_in_bytes #=> Integer
resp.results[0].endpoint_info.peak_requests_per_second #=> Integer
resp.results[0].endpoint_info.created_at #=> Time
resp.results[0].endpoint_info.endpoint_url #=> String
resp.results[0].endpoint_info.endpoint_status #=> String, one of "NONE", "READY", "UPDATING", "FAILED"
resp.results[0].training_parameters #=> Hash
resp.results[0].training_parameters["StringType"] #=> String
resp.results[0].input_data_location_s3 #=> String
resp.results[0].algorithm #=> String, one of "sgd"
resp.results[0].ml_model_type #=> String, one of "REGRESSION", "BINARY", "MULTICLASS"
resp.results[0].score_threshold #=> Float
resp.results[0].score_threshold_last_updated_at #=> Time
resp.results[0].message #=> String
resp.results[0].compute_time #=> Integer
resp.results[0].finished_at #=> Time
resp.results[0].started_at #=> Time
resp.next_token #=> String

Options Hash (options):

:filter_variable (String) —
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.
:eq (String) —
The equal to operator. The MLModel results will have FilterVariable values that exactly match the value specified with EQ.
:gt (String) —
The greater than operator. The MLModel results will have FilterVariable values that are greater than the value specified with GT.
:lt (String) —
The less than operator. The MLModel results will have FilterVariable values that are less than the value specified with LT.
:ge (String) —
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.
:le (String) —
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.
:ne (String) —
The not equal to operator. The MLModel results will have FilterVariable values not equal to the value specified with NE.
:prefix (String) —
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
:sort_order (String) —
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.
:next_token (String) —
The ID of the page in the paginated results.
:limit (Integer) —
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.

Returns:

(Types::DescribeMLModelsOutput) —
Returns a response object which responds to the following methods:
- #results => Array<Types::MLModel>
- #next_token => String

#describe_tags(options = {}) ⇒ `Types::DescribeTagsOutput`

Describes one or more of the tags for your Amazon ML object.

Examples:

Request syntax with placeholder values


resp = client.describe_tags({
  resource_id: "EntityId", # required
  resource_type: "BatchPrediction", # required, accepts BatchPrediction, DataSource, Evaluation, MLModel
})

Response structure


resp.resource_id #=> String
resp.resource_type #=> String, one of "BatchPrediction", "DataSource", "Evaluation", "MLModel"
resp.tags #=> Array
resp.tags[0].key #=> String
resp.tags[0].value #=> String

Options Hash (options):

:resource_id (required, String) —
The ID of the ML object. For example, exampleModelId.
:resource_type (required, String) —
The type of the ML object.

Returns:

(Types::DescribeTagsOutput) —
Returns a response object which responds to the following methods:
- #resource_id => String
- #resource_type => String
- #tags => Array<Types::Tag>

#get_batch_prediction(options = {}) ⇒ `Types::GetBatchPredictionOutput`

Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request.

Examples:

Request syntax with placeholder values


resp = client.get_batch_prediction({
  batch_prediction_id: "EntityId", # required
})

Response structure


resp.batch_prediction_id #=> String
resp.ml_model_id #=> String
resp.batch_prediction_data_source_id #=> String
resp.input_data_location_s3 #=> String
resp.created_by_iam_user #=> String
resp.created_at #=> Time
resp.last_updated_at #=> Time
resp.name #=> String
resp.status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED"
resp.output_uri #=> String
resp.log_uri #=> String
resp.message #=> String
resp.compute_time #=> Integer
resp.finished_at #=> Time
resp.started_at #=> Time
resp.total_record_count #=> Integer
resp.invalid_record_count #=> Integer

Options Hash (options):

:batch_prediction_id (required, String) —
An ID assigned to the BatchPrediction at creation.

Returns:

(Types::GetBatchPredictionOutput) —
Returns a response object which responds to the following methods:
- #batch_prediction_id => String
- #ml_model_id => String
- #batch_prediction_data_source_id => String
- #input_data_location_s3 => String
- #created_by_iam_user => String
- #created_at => Time
- #last_updated_at => Time
- #name => String
- #status => String
- #output_uri => String
- #log_uri => String
- #message => String
- #compute_time => Integer
- #finished_at => Time
- #started_at => Time
- #total_record_count => Integer
- #invalid_record_count => Integer

#get_data_source(options = {}) ⇒ `Types::GetDataSourceOutput`

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.

Examples:

Request syntax with placeholder values


resp = client.get_data_source({
  data_source_id: "EntityId", # required
  verbose: false,
})

Response structure


resp.data_source_id #=> String
resp.data_location_s3 #=> String
resp.data_rearrangement #=> String
resp.created_by_iam_user #=> String
resp.created_at #=> Time
resp.last_updated_at #=> Time
resp.data_size_in_bytes #=> Integer
resp.number_of_files #=> Integer
resp.name #=> String
resp.status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED"
resp.log_uri #=> String
resp.message #=> String
resp.redshift_metadata.redshift_database.database_name #=> String
resp.redshift_metadata.redshift_database.cluster_identifier #=> String
resp.redshift_metadata.database_user_name #=> String
resp.redshift_metadata.select_sql_query #=> String
resp.rds_metadata.database.instance_identifier #=> String
resp.rds_metadata.database.database_name #=> String
resp.rds_metadata.database_user_name #=> String
resp.rds_metadata.select_sql_query #=> String
resp.rds_metadata.resource_role #=> String
resp.rds_metadata.service_role #=> String
resp.rds_metadata.data_pipeline_id #=> String
resp.role_arn #=> String
resp.compute_statistics #=> true/false
resp.compute_time #=> Integer
resp.finished_at #=> Time
resp.started_at #=> Time
resp.data_source_schema #=> String

Options Hash (options):

:data_source_id (required, String) —
The ID assigned to the DataSource at creation.
:verbose (Boolean) —
Specifies whether the GetDataSource operation should return DataSourceSchema.

If true, DataSourceSchema is returned.

If false, DataSourceSchema is not returned.

Returns:

(Types::GetDataSourceOutput) —
Returns a response object which responds to the following methods:
- #data_source_id => String
- #data_location_s3 => String
- #data_rearrangement => String
- #created_by_iam_user => String
- #created_at => Time
- #last_updated_at => Time
- #data_size_in_bytes => Integer
- #number_of_files => Integer
- #name => String
- #status => String
- #log_uri => String
- #message => String
- #redshift_metadata => Types::RedshiftMetadata
- #rds_metadata => Types::RDSMetadata
- #role_arn => String
- #compute_statistics => Boolean
- #compute_time => Integer
- #finished_at => Time
- #started_at => Time
- #data_source_schema => String

#get_evaluation(options = {}) ⇒ `Types::GetEvaluationOutput`

Returns an Evaluation that includes metadata as well as the current status of the Evaluation.

Examples:

Request syntax with placeholder values


resp = client.get_evaluation({
  evaluation_id: "EntityId", # required
})

Response structure


resp.evaluation_id #=> String
resp.ml_model_id #=> String
resp.evaluation_data_source_id #=> String
resp.input_data_location_s3 #=> String
resp.created_by_iam_user #=> String
resp.created_at #=> Time
resp.last_updated_at #=> Time
resp.name #=> String
resp.status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED"
resp.performance_metrics.properties #=> Hash
resp.performance_metrics.properties["PerformanceMetricsPropertyKey"] #=> String
resp.log_uri #=> String
resp.message #=> String
resp.compute_time #=> Integer
resp.finished_at #=> Time
resp.started_at #=> Time

Options Hash (options):

:evaluation_id (required, String) —
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.

Returns:

(Types::GetEvaluationOutput) —
Returns a response object which responds to the following methods:
- #evaluation_id => String
- #ml_model_id => String
- #evaluation_data_source_id => String
- #input_data_location_s3 => String
- #created_by_iam_user => String
- #created_at => Time
- #last_updated_at => Time
- #name => String
- #status => String
- #performance_metrics => Types::PerformanceMetrics
- #log_uri => String
- #message => String
- #compute_time => Integer
- #finished_at => Time
- #started_at => Time

#get_ml_model(options = {}) ⇒ `Types::GetMLModelOutput`

Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel.

GetMLModel provides results in normal or verbose format.

Examples:

Request syntax with placeholder values


resp = client.get_ml_model({
  ml_model_id: "EntityId", # required
  verbose: false,
})

Response structure


resp.ml_model_id #=> String
resp.training_data_source_id #=> String
resp.created_by_iam_user #=> String
resp.created_at #=> Time
resp.last_updated_at #=> Time
resp.name #=> String
resp.status #=> String, one of "PENDING", "INPROGRESS", "FAILED", "COMPLETED", "DELETED"
resp.size_in_bytes #=> Integer
resp.endpoint_info.peak_requests_per_second #=> Integer
resp.endpoint_info.created_at #=> Time
resp.endpoint_info.endpoint_url #=> String
resp.endpoint_info.endpoint_status #=> String, one of "NONE", "READY", "UPDATING", "FAILED"
resp.training_parameters #=> Hash
resp.training_parameters["StringType"] #=> String
resp.input_data_location_s3 #=> String
resp.ml_model_type #=> String, one of "REGRESSION", "BINARY", "MULTICLASS"
resp.score_threshold #=> Float
resp.score_threshold_last_updated_at #=> Time
resp.log_uri #=> String
resp.message #=> String
resp.compute_time #=> Integer
resp.finished_at #=> Time
resp.started_at #=> Time
resp.recipe #=> String
resp.schema #=> String

Options Hash (options):

:ml_model_id (required, String) —
The ID assigned to the MLModel at creation.
:verbose (Boolean) —
Specifies whether the GetMLModel operation should return Recipe.

If true, Recipe is returned.

If false, Recipe is not returned.

Returns:

(Types::GetMLModelOutput) —
Returns a response object which responds to the following methods:
- #ml_model_id => String
- #training_data_source_id => String
- #created_by_iam_user => String
- #created_at => Time
- #last_updated_at => Time
- #name => String
- #status => String
- #size_in_bytes => Integer
- #endpoint_info => Types::RealtimeEndpointInfo
- #training_parameters => Hash<String,String>
- #input_data_location_s3 => String
- #ml_model_type => String
- #score_threshold => Float
- #score_threshold_last_updated_at => Time
- #log_uri => String
- #message => String
- #compute_time => Integer
- #finished_at => Time
- #started_at => Time
- #recipe => String
- #schema => String

#predict(options = {}) ⇒ `Types::PredictOutput`

Generates a prediction for the observation using the specified ML Model.

Note

Not all response parameters will be populated. Whether a response parameter is populated depends on the type of model requested.

Examples:

Request syntax with placeholder values


resp = client.predict({
  ml_model_id: "EntityId", # required
  record: { # required
    "VariableName" => "VariableValue",
  },
  predict_endpoint: "VipURL", # required
})

Response structure


resp.prediction.predicted_label #=> String
resp.prediction.predicted_value #=> Float
resp.prediction.predicted_scores #=> Hash
resp.prediction.predicted_scores["Label"] #=> Float
resp.prediction.details #=> Hash
resp.prediction.details["DetailsAttributes"] #=> String

Options Hash (options):

:ml_model_id (required, String) —
A unique identifier of the MLModel.
:record (required, Hash<String,String>) —
A map of variable name-value pairs that represent an observation.
:predict_endpoint (required, String)

Returns:

(Types::PredictOutput) —
Returns a response object which responds to the following methods:
- #prediction => Types::Prediction

#update_batch_prediction(options = {}) ⇒ `Types::UpdateBatchPredictionOutput`

Updates the BatchPredictionName of a BatchPrediction.

You can use the GetBatchPrediction operation to view the contents of the updated data element.

Examples:

Request syntax with placeholder values


resp = client.update_batch_prediction({
  batch_prediction_id: "EntityId", # required
  batch_prediction_name: "EntityName", # required
})

Response structure


resp.batch_prediction_id #=> String

Options Hash (options):

:batch_prediction_id (required, String) —
The ID assigned to the BatchPrediction during creation.
:batch_prediction_name (required, String) —
A new user-supplied name or description of the BatchPrediction.

Returns:

(Types::UpdateBatchPredictionOutput) —
Returns a response object which responds to the following methods:
- #batch_prediction_id => String

#update_data_source(options = {}) ⇒ `Types::UpdateDataSourceOutput`

Updates the DataSourceName of a DataSource.

You can use the GetDataSource operation to view the contents of the updated data element.

Examples:

Request syntax with placeholder values


resp = client.update_data_source({
  data_source_id: "EntityId", # required
  data_source_name: "EntityName", # required
})

Response structure


resp.data_source_id #=> String

Options Hash (options):

:data_source_id (required, String) —
The ID assigned to the DataSource during creation.
:data_source_name (required, String) —
A new user-supplied name or description of the DataSource that will replace the current description.

Returns:

(Types::UpdateDataSourceOutput) —
Returns a response object which responds to the following methods:
- #data_source_id => String

#update_evaluation(options = {}) ⇒ `Types::UpdateEvaluationOutput`

Updates the EvaluationName of an Evaluation.

You can use the GetEvaluation operation to view the contents of the updated data element.

Examples:

Request syntax with placeholder values


resp = client.update_evaluation({
  evaluation_id: "EntityId", # required
  evaluation_name: "EntityName", # required
})

Response structure


resp.evaluation_id #=> String

Options Hash (options):

:evaluation_id (required, String) —
The ID assigned to the Evaluation during creation.
:evaluation_name (required, String) —
A new user-supplied name or description of the Evaluation that will replace the current content.

Returns:

(Types::UpdateEvaluationOutput) —
Returns a response object which responds to the following methods:
- #evaluation_id => String

#update_ml_model(options = {}) ⇒ `Types::UpdateMLModelOutput`

Updates the MLModelName and the ScoreThreshold of an MLModel.

You can use the GetMLModel operation to view the contents of the updated data element.

Examples:

Request syntax with placeholder values


resp = client.update_ml_model({
  ml_model_id: "EntityId", # required
  ml_model_name: "EntityName",
  score_threshold: 1.0,
})

Response structure


resp.ml_model_id #=> String

Options Hash (options):

:ml_model_id (required, String) —
The ID assigned to the MLModel during creation.
:ml_model_name (String) —
A user-supplied name or description of the MLModel.
:score_threshold (Float) —
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.

Returns:

(Types::UpdateMLModelOutput) —
Returns a response object which responds to the following methods:
- #ml_model_id => String

#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ `Boolean`

Waiters polls an API operation until a resource enters a desired state.

Basic Usage

Waiters will poll until they are succesful, they fail by entering a terminal state, or until a maximum number of attempts are made.

# polls in a loop, sleeping between attempts client.waiter_until(waiter_name, params)

Configuration

You can configure the maximum number of polling attempts, and the delay (in seconds) between each polling attempt. You configure waiters by passing a block to #wait_until:

# poll for ~25 seconds
client.wait_until(...) do |w|
  w.max_attempts = 5
  w.delay = 5
end

Callbacks

You can be notified before each polling attempt and before each delay. If you throw :success or :failure from these callbacks, it will terminate the waiter.

started_at = Time.now
client.wait_until(...) do |w|

  # disable max attempts
  w.max_attempts = nil

  # poll for 1 hour, instead of a number of attempts
  w.before_wait do |attempts, response|
    throw :failure if Time.now - started_at > 3600
  end

end

Handling Errors

When a waiter is successful, it returns true. When a waiter fails, it raises an error. All errors raised extend from Waiters::Errors::WaiterFailed.

begin
  client.wait_until(...)
rescue Aws::Waiters::Errors::WaiterFailed
  # resource did not enter the desired state in time
end

Parameters:

waiter_name (Symbol) —
The name of the waiter. See #waiter_names for a full list of supported waiters.
params (Hash) (defaults to: {}) —
Additional request parameters. See the #waiter_names for a list of supported waiters and what request they call. The called request determines the list of accepted parameters.

Yield Parameters:

waiter (Waiters::Waiter) —
Yields a Waiter object that can be configured prior to waiting.

Returns:

(Boolean) —
Returns true if the waiter was successful.

Raises:

(Errors::FailureStateError) —
Raised when the waiter terminates because the waiter has entered a state that it will not transition out of, preventing success.
(Errors::TooManyAttemptsError) —
Raised when the configured maximum number of attempts have been made, and the waiter is not yet successful.
(Errors::UnexpectedError) —
Raised when an error is encounted while polling for a resource that is not expected.
(Errors::NoSuchWaiterError) —
Raised when you request to wait for an unknown state.

#waiter_names ⇒ `Array<Symbol>`

Returns the list of supported waiters. The following table lists the supported waiters and the client method they call:

Waiter Name	Client Method	Default Delay:	Default Max Attempts:
`:batch_prediction_available`	#describe_batch_predictions	30	60
`:data_source_available`	#describe_data_sources	30	60
`:evaluation_available`	#describe_evaluations	30	60
`:ml_model_available`	#describe_ml_models	30	60

Returns:

(Array<Symbol>) —
the list of supported waiters.

Class: Aws::MachineLearning::Client

Overview

Region

Credentials

Instance Attribute Summary

Attributes inherited from Seahorse::Client::Base

Constructor collapse

API Operations collapse

Instance Method Summary collapse

Methods inherited from Seahorse::Client::Base

Methods included from Seahorse::Client::HandlerBuilder

Constructor Details

#initialize(options = {}) ⇒ Aws::MachineLearning::Client

Instance Method Details

#add_tags(options = {}) ⇒ Types::AddTagsOutput

#create_batch_prediction(options = {}) ⇒ Types::CreateBatchPredictionOutput

#create_data_source_from_rds(options = {}) ⇒ Types::CreateDataSourceFromRDSOutput

#create_data_source_from_redshift(options = {}) ⇒ Types::CreateDataSourceFromRedshiftOutput

#create_data_source_from_s3(options = {}) ⇒ Types::CreateDataSourceFromS3Output

#create_evaluation(options = {}) ⇒ Types::CreateEvaluationOutput

#create_ml_model(options = {}) ⇒ Types::CreateMLModelOutput

#create_realtime_endpoint(options = {}) ⇒ Types::CreateRealtimeEndpointOutput

#delete_batch_prediction(options = {}) ⇒ Types::DeleteBatchPredictionOutput

#delete_data_source(options = {}) ⇒ Types::DeleteDataSourceOutput

#delete_evaluation(options = {}) ⇒ Types::DeleteEvaluationOutput

#delete_ml_model(options = {}) ⇒ Types::DeleteMLModelOutput

#delete_realtime_endpoint(options = {}) ⇒ Types::DeleteRealtimeEndpointOutput

#delete_tags(options = {}) ⇒ Types::DeleteTagsOutput

#describe_batch_predictions(options = {}) ⇒ Types::DescribeBatchPredictionsOutput

#describe_data_sources(options = {}) ⇒ Types::DescribeDataSourcesOutput

#describe_evaluations(options = {}) ⇒ Types::DescribeEvaluationsOutput

#describe_ml_models(options = {}) ⇒ Types::DescribeMLModelsOutput

#describe_tags(options = {}) ⇒ Types::DescribeTagsOutput

#get_batch_prediction(options = {}) ⇒ Types::GetBatchPredictionOutput

#get_data_source(options = {}) ⇒ Types::GetDataSourceOutput

#get_evaluation(options = {}) ⇒ Types::GetEvaluationOutput

#get_ml_model(options = {}) ⇒ Types::GetMLModelOutput

#predict(options = {}) ⇒ Types::PredictOutput

#update_batch_prediction(options = {}) ⇒ Types::UpdateBatchPredictionOutput

#update_data_source(options = {}) ⇒ Types::UpdateDataSourceOutput

#update_evaluation(options = {}) ⇒ Types::UpdateEvaluationOutput

#update_ml_model(options = {}) ⇒ Types::UpdateMLModelOutput

#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ Boolean

Basic Usage

Configuration

Callbacks

Handling Errors

#waiter_names ⇒ Array<Symbol>

#initialize(options = {}) ⇒ `Aws::MachineLearning::Client`

#add_tags(options = {}) ⇒ `Types::AddTagsOutput`

#create_batch_prediction(options = {}) ⇒ `Types::CreateBatchPredictionOutput`

#create_data_source_from_rds(options = {}) ⇒ `Types::CreateDataSourceFromRDSOutput`

#create_data_source_from_redshift(options = {}) ⇒ `Types::CreateDataSourceFromRedshiftOutput`

#create_data_source_from_s3(options = {}) ⇒ `Types::CreateDataSourceFromS3Output`

#create_evaluation(options = {}) ⇒ `Types::CreateEvaluationOutput`

#create_ml_model(options = {}) ⇒ `Types::CreateMLModelOutput`

#create_realtime_endpoint(options = {}) ⇒ `Types::CreateRealtimeEndpointOutput`

#delete_batch_prediction(options = {}) ⇒ `Types::DeleteBatchPredictionOutput`

#delete_data_source(options = {}) ⇒ `Types::DeleteDataSourceOutput`

#delete_evaluation(options = {}) ⇒ `Types::DeleteEvaluationOutput`

#delete_ml_model(options = {}) ⇒ `Types::DeleteMLModelOutput`

#delete_realtime_endpoint(options = {}) ⇒ `Types::DeleteRealtimeEndpointOutput`

#delete_tags(options = {}) ⇒ `Types::DeleteTagsOutput`

#describe_batch_predictions(options = {}) ⇒ `Types::DescribeBatchPredictionsOutput`

#describe_data_sources(options = {}) ⇒ `Types::DescribeDataSourcesOutput`

#describe_evaluations(options = {}) ⇒ `Types::DescribeEvaluationsOutput`

#describe_ml_models(options = {}) ⇒ `Types::DescribeMLModelsOutput`

#describe_tags(options = {}) ⇒ `Types::DescribeTagsOutput`

#get_batch_prediction(options = {}) ⇒ `Types::GetBatchPredictionOutput`

#get_data_source(options = {}) ⇒ `Types::GetDataSourceOutput`

#get_evaluation(options = {}) ⇒ `Types::GetEvaluationOutput`

#get_ml_model(options = {}) ⇒ `Types::GetMLModelOutput`

#predict(options = {}) ⇒ `Types::PredictOutput`

#update_batch_prediction(options = {}) ⇒ `Types::UpdateBatchPredictionOutput`

#update_data_source(options = {}) ⇒ `Types::UpdateDataSourceOutput`

#update_evaluation(options = {}) ⇒ `Types::UpdateEvaluationOutput`

#update_ml_model(options = {}) ⇒ `Types::UpdateMLModelOutput`

#wait_until(waiter_name, params = {}) {|waiter| ... } ⇒ `Boolean`

#waiter_names ⇒ `Array<Symbol>`