Amazon Lookout for Equipment¶

Description¶

Amazon Lookout for Equipment is a machine learning service that uses advanced analytics to identify anomalies in machines from sensor data for use in predictive maintenance.

Usage¶

lookoutequipment(
  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 <- lookoutequipment(
  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_dataset`	Creates a container for a collection of data being ingested for analysis
`create_inference_scheduler`	Creates a scheduled inference
`create_label`	Creates a label for an event
`create_label_group`	Creates a group of labels
`create_model`	Creates a machine learning model for data inference
`create_retraining_scheduler`	Creates a retraining scheduler on the specified model
`delete_dataset`	Deletes a dataset and associated artifacts
`delete_inference_scheduler`	Deletes an inference scheduler that has been set up
`delete_label`	Deletes a label
`delete_label_group`	Deletes a group of labels
`delete_model`	Deletes a machine learning model currently available for Amazon Lookout for Equipment
`delete_resource_policy`	Deletes the resource policy attached to the resource
`delete_retraining_scheduler`	Deletes a retraining scheduler from a model
`describe_data_ingestion_job`	Provides information on a specific data ingestion job such as creation time, dataset ARN, and status
`describe_dataset`	Provides a JSON description of the data in each time series dataset, including names, column names, and data types
`describe_inference_scheduler`	Specifies information about the inference scheduler being used, including name, model, status, and associated metadata
`describe_label`	Returns the name of the label
`describe_label_group`	Returns information about the label group
`describe_model`	Provides a JSON containing the overall information about a specific machine learning model, including model name and ARN, dataset, training and evaluation information, status, and so on
`describe_model_version`	Retrieves information about a specific machine learning model version
`describe_resource_policy`	Provides the details of a resource policy attached to a resource
`describe_retraining_scheduler`	Provides a description of the retraining scheduler, including information such as the model name and retraining parameters
`import_dataset`	Imports a dataset
`import_model_version`	Imports a model that has been trained successfully
`list_data_ingestion_jobs`	Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on
`list_datasets`	Lists all datasets currently available in your account, filtering on the dataset name
`list_inference_events`	Lists all inference events that have been found for the specified inference scheduler
`list_inference_executions`	Lists all inference executions that have been performed by the specified inference scheduler
`list_inference_schedulers`	Retrieves a list of all inference schedulers currently available for your account
`list_label_groups`	Returns a list of the label groups
`list_labels`	Provides a list of labels
`list_models`	Generates a list of all models in the account, including model name and ARN, dataset, and status
`list_model_versions`	Generates a list of all model versions for a given model, including the model version, model version ARN, and status
`list_retraining_schedulers`	Lists all retraining schedulers in your account, filtering by model name prefix and status
`list_sensor_statistics`	Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset
`list_tags_for_resource`	Lists all the tags for a specified resource, including key and value
`put_resource_policy`	Creates a resource control policy for a given resource
`start_data_ingestion_job`	Starts a data ingestion job
`start_inference_scheduler`	Starts an inference scheduler
`start_retraining_scheduler`	Starts a retraining scheduler
`stop_inference_scheduler`	Stops an inference scheduler
`stop_retraining_scheduler`	Stops a retraining scheduler
`tag_resource`	Associates a given tag to a resource in your account
`untag_resource`	Removes a specific tag from a given resource
`update_active_model_version`	Sets the active model version for a given machine learning model
`update_inference_scheduler`	Updates an inference scheduler
`update_label_group`	Updates the label group
`update_model`	Updates a model in the account
`update_retraining_scheduler`	Updates a retraining scheduler

Examples¶

svc <- lookoutequipment()
svc$create_dataset(
  Foo = 123
)