machinelearning: Amazon Machine Learning

View source: R/machinelearning_service.R

machinelearningR 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)


paws.machine.learning documentation built on Sept. 12, 2024, 6:23 a.m.