Client

lookoutequipment

R Documentation

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

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"
  ),
  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¶

## Not run: 
svc <- lookoutequipment()
# 
svc$create_retraining_scheduler(
  ClientToken = "sample-client-token",
  LookbackWindow = "P360D",
  ModelName = "sample-model",
  PromoteMode = "MANUAL",
  RetrainingFrequency = "P1M"
)

## End(Not run)