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.
cleanroomsml(
config = list(),
credentials = list(),
endpoint = NULL,
region = NULL
)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.
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
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.
Optional shorthand for complete URL to use for the constructed client.
Optional shorthand for AWS Region used in instantiating the client.
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"
)
| 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 |
| create_configured_model_algorithm_association | 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 |
| delete_configured_audience_model_policy | Deletes the specified configured audience model policy |
| delete_configured_model_algorithm | Deletes a configured model algorithm |
| delete_configured_model_algorithm_association | 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 |
| get_collaboration_configured_model_algorithm_association | 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 |
| get_configured_model_algorithm_association | 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 |
| list_collaboration_configured_model_algorithm_associations | 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 |
| list_collaboration_trained_model_export_jobs | Returns a list of the export jobs for a trained model in a collaboration |
| list_collaboration_trained_model_inference_jobs | 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 |
| list_configured_model_algorithm_associations | 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 |
if (FALSE) {
svc <- cleanroomsml()
svc$cancel_trained_model(
Foo = 123
)
}
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