Amazon Personalize¶

Description¶

Amazon Personalize is a machine learning service that makes it easy to add individualized recommendations to customers.

Usage¶

personalize(
  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
- use_dual_stack: Set this to true to use the dualstack (IPv4 and IPv6) endpoint for a service, where available, falling back to the regular endpoint when it isn't. Defaults to the AWS_USE_DUALSTACK_ENDPOINT environment variable when unset.
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 <- personalize(
  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",
    use_dual_stack = "logical"
  ),
  credentials = list(
    creds = list(
      access_key_id = "string",
      secret_access_key = "string",
      session_token = "string"
    ),
    profile = "string",
    anonymous = "logical"
  ),
  endpoint = "string",
  region = "string"
)

Operations¶

`create_batch_inference_job`	Generates batch recommendations based on a list of items or users stored in Amazon S3 and exports the recommendations to an Amazon S3 bucket
`create_batch_segment_job`	Creates a batch segment job
`create_campaign`	You incur campaign costs while it is active
`create_data_deletion_job`	Creates a batch job that deletes all references to specific users from an Amazon Personalize dataset group in batches
`create_dataset`	Creates an empty dataset and adds it to the specified dataset group
`create_dataset_export_job`	Creates a job that exports data from your dataset to an Amazon S3 bucket
`create_dataset_group`	Creates an empty dataset group
`create_dataset_import_job`	Creates a job that imports training data from your data source (an Amazon S3 bucket) to an Amazon Personalize dataset
`create_event_tracker`	Creates an event tracker that you use when adding event data to a specified dataset group using the PutEvents API
`create_filter`	Creates a recommendation filter
`create_metric_attribution`	Creates a metric attribution
`create_recommender`	Creates a recommender with the recipe (a Domain dataset group use case) you specify
`create_schema`	Creates an Amazon Personalize schema from the specified schema string
`create_solution`	By default, all new solutions use automatic training
`create_solution_version`	Trains or retrains an active solution in a Custom dataset group
`delete_campaign`	Removes a campaign by deleting the solution deployment
`delete_dataset`	Deletes a dataset
`delete_dataset_group`	Deletes a dataset group
`delete_event_tracker`	Deletes the event tracker
`delete_filter`	Deletes a filter
`delete_metric_attribution`	Deletes a metric attribution
`delete_recommender`	Deactivates and removes a recommender
`delete_schema`	Deletes a schema
`delete_solution`	Deletes all versions of a solution and the Solution object itself
`describe_algorithm`	Describes the given algorithm
`describe_batch_inference_job`	Gets the properties of a batch inference job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate the recommendations
`describe_batch_segment_job`	Gets the properties of a batch segment job including name, Amazon Resource Name (ARN), status, input and output configurations, and the ARN of the solution version used to generate segments
`describe_campaign`	Describes the given campaign, including its status
`describe_data_deletion_job`	Describes the data deletion job created by CreateDataDeletionJob, including the job status
`describe_dataset`	Describes the given dataset
`describe_dataset_export_job`	Describes the dataset export job created by CreateDatasetExportJob, including the export job status
`describe_dataset_group`	Describes the given dataset group
`describe_dataset_import_job`	Describes the dataset import job created by CreateDatasetImportJob, including the import job status
`describe_event_tracker`	Describes an event tracker
`describe_feature_transformation`	Describes the given feature transformation
`describe_filter`	Describes a filter's properties
`describe_metric_attribution`	Describes a metric attribution
`describe_recipe`	Describes a recipe
`describe_recommender`	Describes the given recommender, including its status
`describe_schema`	Describes a schema
`describe_solution`	Describes a solution
`describe_solution_version`	Describes a specific version of a solution
`get_solution_metrics`	Gets the metrics for the specified solution version
`list_batch_inference_jobs`	Gets a list of the batch inference jobs that have been performed off of a solution version
`list_batch_segment_jobs`	Gets a list of the batch segment jobs that have been performed off of a solution version that you specify
`list_campaigns`	Returns a list of campaigns that use the given solution
`list_data_deletion_jobs`	Returns a list of data deletion jobs for a dataset group ordered by creation time, with the most recent first
`list_dataset_export_jobs`	Returns a list of dataset export jobs that use the given dataset
`list_dataset_groups`	Returns a list of dataset groups
`list_dataset_import_jobs`	Returns a list of dataset import jobs that use the given dataset
`list_datasets`	Returns the list of datasets contained in the given dataset group
`list_event_trackers`	Returns the list of event trackers associated with the account
`list_filters`	Lists all filters that belong to a given dataset group
`list_metric_attribution_metrics`	Lists the metrics for the metric attribution
`list_metric_attributions`	Lists metric attributions
`list_recipes`	Returns a list of available recipes
`list_recommenders`	Returns a list of recommenders in a given Domain dataset group
`list_schemas`	Returns the list of schemas associated with the account
`list_solutions`	Returns a list of solutions in a given dataset group
`list_solution_versions`	Returns a list of solution versions for the given solution
`list_tags_for_resource`	Get a list of tags attached to a resource
`start_recommender`	Starts a recommender that is INACTIVE
`stop_recommender`	Stops a recommender that is ACTIVE
`stop_solution_version_creation`	Stops creating a solution version that is in a state of CREATE_PENDING or CREATE IN_PROGRESS
`tag_resource`	Add a list of tags to a resource
`untag_resource`	Removes the specified tags that are attached to a resource
`update_campaign`	Updates a campaign to deploy a retrained solution version with an existing campaign, change your campaign's minProvisionedTPS, or modify your campaign's configuration
`update_dataset`	Update a dataset to replace its schema with a new or existing one
`update_metric_attribution`	Updates a metric attribution
`update_recommender`	Updates the recommender to modify the recommender configuration
`update_solution`	Updates an Amazon Personalize solution to use a different automatic training configuration

Examples¶

svc <- personalize()
svc$create_batch_inference_job(
  Foo = 123
)