Client

cleanroomsml

R Documentation

AWS Clean Rooms ML¶

Description¶

Welcome to the Amazon Web Services Clean Rooms ML API Reference.

Amazon Web Services Clean Rooms ML provides a privacy-enhancing method for two parties to identify similar users in their data without the need to share their data with each other. The first party brings the training data to Clean Rooms so that they can create and configure an audience model (lookalike model) and associate it with a collaboration. The second party then brings their seed data to Clean Rooms and generates an audience (lookalike segment) that resembles the training data.

To learn more about Amazon Web Services Clean Rooms ML concepts, procedures, and best practices, see the Clean Rooms User Guide.

To learn more about SQL commands, functions, and conditions supported in Clean Rooms, see the Clean Rooms SQL Reference.

Usage¶

cleanroomsml(
  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 <- cleanroomsml(
  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¶

cancel_trained_model: Submits a request to cancel the trained model job
cancel_trained_model_inference_job: Submits a request to cancel a trained model inference job
create_audience_model: Defines the information necessary to create an audience model
create_configured_audience_model: Defines the information necessary to create a configured audience model
create_configured_model_algorithm: Creates a configured model algorithm using a container image stored in an ECR repository; Associates a configured model algorithm to a collaboration for use by any member of the collaboration
create_ml_input_channel: Provides the information to create an ML input channel
create_trained_model: Creates a trained model from an associated configured model algorithm using data from any member of the collaboration
create_training_dataset: Defines the information necessary to create a training dataset
delete_audience_generation_job: Deletes the specified audience generation job, and removes all data associated with the job
delete_audience_model: Specifies an audience model that you want to delete
delete_configured_audience_model: Deletes the specified configured audience model; Deletes the specified configured audience model policy
delete_configured_model_algorithm: Deletes a configured model algorithm; Deletes a configured model algorithm association
delete_ml_configuration: Deletes a ML modeling configuration
delete_ml_input_channel_data: Provides the information necessary to delete an ML input channel
delete_trained_model_output: Deletes the output of a trained model
delete_training_dataset: Specifies a training dataset that you want to delete
get_audience_generation_job: Returns information about an audience generation job
get_audience_model: Returns information about an audience model; Returns information about the configured model algorithm association in a collaboration
get_collaboration_ml_input_channel: Returns information about a specific ML input channel in a collaboration
get_collaboration_trained_model: Returns information about a trained model in a collaboration
get_configured_audience_model: Returns information about a specified configured audience model
get_configured_audience_model_policy: Returns information about a configured audience model policy
get_configured_model_algorithm: Returns information about a configured model algorithm; Returns information about a configured model algorithm association
get_ml_configuration: Returns information about a specific ML configuration
get_ml_input_channel: Returns information about an ML input channel
get_trained_model: Returns information about a trained model
get_trained_model_inference_job: Returns information about a trained model inference job
get_training_dataset: Returns information about a training dataset
list_audience_export_jobs: Returns a list of the audience export jobs
list_audience_generation_jobs: Returns a list of audience generation jobs
list_audience_models: Returns a list of audience models; Returns a list of the configured model algorithm associations in a collaboration
list_collaboration_ml_input_channels: Returns a list of the ML input channels in a collaboration; Returns a list of the export jobs for a trained model in a collaboration; Returns a list of trained model inference jobs in a specified collaboration
list_collaboration_trained_models: Returns a list of the trained models in a collaboration
list_configured_audience_models: Returns a list of the configured audience models; Returns a list of configured model algorithm associations
list_configured_model_algorithms: Returns a list of configured model algorithms
list_ml_input_channels: Returns a list of ML input channels
list_tags_for_resource: Returns a list of tags for a provided resource
list_trained_model_inference_jobs: Returns a list of trained model inference jobs that match the request parameters
list_trained_models: Returns a list of trained models
list_training_datasets: Returns a list of training datasets
put_configured_audience_model_policy: Create or update the resource policy for a configured audience model
put_ml_configuration: Assigns information about an ML configuration
start_audience_export_job: Export an audience of a specified size after you have generated an audience
start_audience_generation_job: Information necessary to start the audience generation job
start_trained_model_export_job: Provides the information necessary to start a trained model export job
start_trained_model_inference_job: Defines the information necessary to begin a trained model inference job
tag_resource: Adds metadata tags to a specified resource
untag_resource: Removes metadata tags from a specified resource
update_configured_audience_model: Provides the information necessary to update a configured audience model

Examples¶

## Not run: 
svc <- cleanroomsml()
svc$cancel_trained_model(
  Foo = 123
)

## End(Not run)