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.
lookoutequipment(
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 <- 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"
)
| 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 |
if (FALSE) {
svc <- lookoutequipment()
svc$create_dataset(
Foo = 123
)
}
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