Client

machinelearning

R Documentation

Amazon Machine Learning¶

Description¶

Definition of the public APIs exposed by Amazon Machine Learning

Usage¶

machinelearning(
  config = list(),
  credentials = list(),
  endpoint = NULL,
  region = NULL
)

Arguments¶

config

Optional configuration of credentials, endpoint, and/or region.

credentials:
- creds:
  - access_key_id: AWS access key ID
  - secret_access_key: AWS secret access key
  - session_token: AWS temporary session token
- profile: The name of a profile to use. If not given, then the default profile is used.
- anonymous: Set anonymous credentials.
endpoint: The complete URL to use for the constructed client.
region: The AWS Region used in instantiating the client.
close_connection: Immediately close all HTTP connections.
timeout: The time in seconds till a timeout exception is thrown when attempting to make a connection. The default is 60 seconds.
s3_force_path_style: Set this to true to force the request to use path-style addressing, i.e. ⁠http://s3.amazonaws.com/BUCKET/KEY⁠.
sts_regional_endpoint: Set sts regional endpoint resolver to regional or legacy https://docs.aws.amazon.com/sdkref/latest/guide/feature-sts-regionalized-endpoints.html

credentials

Optional credentials shorthand for the config parameter

creds:
- access_key_id: AWS access key ID
- secret_access_key: AWS secret access key
- session_token: AWS temporary session token
profile: The name of a profile to use. If not given, then the default profile is used.
anonymous: Set anonymous credentials.

endpoint

Optional shorthand for complete URL to use for the constructed client.

region

Optional shorthand for AWS Region used in instantiating the client.

Value¶

A client for the service. You can call the service's operations using syntax like svc$operation(...), where svc is the name you've assigned to the client. The available operations are listed in the Operations section.

Service syntax¶

svc <- machinelearning(
  config = list(
    credentials = list(
      creds = list(
        access_key_id = "string",
        secret_access_key = "string",
        session_token = "string"
      ),
      profile = "string",
      anonymous = "logical"
    ),
    endpoint = "string",
    region = "string",
    close_connection = "logical",
    timeout = "numeric",
    s3_force_path_style = "logical",
    sts_regional_endpoint = "string"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations¶

add_tags: Adds one or more tags to an object, up to a limit of 10
create_batch_prediction: Generates predictions for a group of observations
create_data_source_from_rds: Creates a DataSource object from an Amazon Relational Database Service (Amazon RDS)
create_data_source_from_redshift: Creates a DataSource from a database hosted on an Amazon Redshift cluster
create_data_source_from_s3: Creates a DataSource object
create_evaluation: Creates a new Evaluation of an MLModel
create_ml_model: Creates a new MLModel using the DataSource and the recipe as information sources
create_realtime_endpoint: Creates a real-time endpoint for the MLModel
delete_batch_prediction: Assigns the DELETED status to a BatchPrediction, rendering it unusable
delete_data_source: Assigns the DELETED status to a DataSource, rendering it unusable
delete_evaluation: Assigns the DELETED status to an Evaluation, rendering it unusable
delete_ml_model: Assigns the DELETED status to an MLModel, rendering it unusable
delete_realtime_endpoint: Deletes a real time endpoint of an MLModel
delete_tags: Deletes the specified tags associated with an ML object
describe_batch_predictions: Returns a list of BatchPrediction operations that match the search criteria in the request
describe_data_sources: Returns a list of DataSource that match the search criteria in the request
describe_evaluations: Returns a list of DescribeEvaluations that match the search criteria in the request
describe_ml_models: Returns a list of MLModel that match the search criteria in the request
describe_tags: Describes one or more of the tags for your Amazon ML object
get_batch_prediction: Returns a BatchPrediction that includes detailed metadata, status, and data file information for a Batch Prediction request
get_data_source: Returns a DataSource that includes metadata and data file information, as well as the current status of the DataSource
get_evaluation: Returns an Evaluation that includes metadata as well as the current status of the Evaluation
get_ml_model: Returns an MLModel that includes detailed metadata, data source information, and the current status of the MLModel
predict: Generates a prediction for the observation using the specified ML Model
update_batch_prediction: Updates the BatchPredictionName of a BatchPrediction
update_data_source: Updates the DataSourceName of a DataSource
update_evaluation: Updates the EvaluationName of an Evaluation
update_ml_model: Updates the MLModelName and the ScoreThreshold of an MLModel

Examples¶

## Not run: 
svc <- machinelearning()
svc$add_tags(
  Foo = 123
)

## End(Not run)