AWS.LookoutEquipment — AWS SDK for JavaScript (original) (raw)

Property Details

endpoint ⇒ AWS.Endpoint

Returns an Endpoint object representing the endpoint URL for service requests.

Method Details

createDataset(params = {}, callback) ⇒ AWS.Request

Creates a container for a collection of data being ingested for analysis. The dataset contains the metadata describing where the data is and what the data actually looks like. For example, it contains the location of the data source, the data schema, and other information. A dataset also contains any tags associated with the ingested data.

createInferenceScheduler(params = {}, callback) ⇒ AWS.Request

Creates a scheduled inference. Scheduling an inference is setting up a continuous real-time inference plan to analyze new measurement data. When setting up the schedule, you provide an S3 bucket location for the input data, assign it a delimiter between separate entries in the data, set an offset delay if desired, and set the frequency of inferencing. You must also provide an S3 bucket location for the output data.

createLabel(params = {}, callback) ⇒ AWS.Request

Creates a label for an event.

createLabelGroup(params = {}, callback) ⇒ AWS.Request

Creates a group of labels.

createModel(params = {}, callback) ⇒ AWS.Request

Creates a machine learning model for data inference.

A machine-learning (ML) model is a mathematical model that finds patterns in your data. In Amazon Lookout for Equipment, the model learns the patterns of normal behavior and detects abnormal behavior that could be potential equipment failure (or maintenance events). The models are made by analyzing normal data and abnormalities in machine behavior that have already occurred.

Your model is trained using a portion of the data from your dataset and uses that data to learn patterns of normal behavior and abnormal patterns that lead to equipment failure. Another portion of the data is used to evaluate the model's accuracy.

createRetrainingScheduler(params = {}, callback) ⇒ AWS.Request

Creates a retraining scheduler on the specified model.

deleteDataset(params = {}, callback) ⇒ AWS.Request

Deletes a dataset and associated artifacts. The operation will check to see if any inference scheduler or data ingestion job is currently using the dataset, and if there isn't, the dataset, its metadata, and any associated data stored in S3 will be deleted. This does not affect any models that used this dataset for training and evaluation, but does prevent it from being used in the future.

deleteInferenceScheduler(params = {}, callback) ⇒ AWS.Request

Deletes an inference scheduler that has been set up. Prior inference results will not be deleted.

deleteLabelGroup(params = {}, callback) ⇒ AWS.Request

Deletes a group of labels.

deleteModel(params = {}, callback) ⇒ AWS.Request

Deletes a machine learning model currently available for Amazon Lookout for Equipment. This will prevent it from being used with an inference scheduler, even one that is already set up.

deleteResourcePolicy(params = {}, callback) ⇒ AWS.Request

Deletes the resource policy attached to the resource.

deleteRetrainingScheduler(params = {}, callback) ⇒ AWS.Request

Deletes a retraining scheduler from a model. The retraining scheduler must be in the STOPPED status.

describeDataIngestionJob(params = {}, callback) ⇒ AWS.Request

Provides information on a specific data ingestion job such as creation time, dataset ARN, and status.

describeDataset(params = {}, callback) ⇒ AWS.Request

Provides a JSON description of the data in each time series dataset, including names, column names, and data types.

describeInferenceScheduler(params = {}, callback) ⇒ AWS.Request

Specifies information about the inference scheduler being used, including name, model, status, and associated metadata

describeLabel(params = {}, callback) ⇒ AWS.Request

Returns the name of the label.

describeLabelGroup(params = {}, callback) ⇒ AWS.Request

Returns information about the label group.

describeModel(params = {}, callback) ⇒ AWS.Request

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.

describeModelVersion(params = {}, callback) ⇒ AWS.Request

Retrieves information about a specific machine learning model version.

describeResourcePolicy(params = {}, callback) ⇒ AWS.Request

Provides the details of a resource policy attached to a resource.

describeRetrainingScheduler(params = {}, callback) ⇒ AWS.Request

Provides a description of the retraining scheduler, including information such as the model name and retraining parameters.

importDataset(params = {}, callback) ⇒ AWS.Request

importModelVersion(params = {}, callback) ⇒ AWS.Request

Imports a model that has been trained successfully.

listDataIngestionJobs(params = {}, callback) ⇒ AWS.Request

Provides a list of all data ingestion jobs, including dataset name and ARN, S3 location of the input data, status, and so on.

listDatasets(params = {}, callback) ⇒ AWS.Request

Lists all datasets currently available in your account, filtering on the dataset name.

listInferenceEvents(params = {}, callback) ⇒ AWS.Request

Lists all inference events that have been found for the specified inference scheduler.

listInferenceExecutions(params = {}, callback) ⇒ AWS.Request

Lists all inference executions that have been performed by the specified inference scheduler.

listInferenceSchedulers(params = {}, callback) ⇒ AWS.Request

Retrieves a list of all inference schedulers currently available for your account.

listLabelGroups(params = {}, callback) ⇒ AWS.Request

Returns a list of the label groups.

listLabels(params = {}, callback) ⇒ AWS.Request

Provides a list of labels.

listModels(params = {}, callback) ⇒ AWS.Request

Generates a list of all models in the account, including model name and ARN, dataset, and status.

listModelVersions(params = {}, callback) ⇒ AWS.Request

Generates a list of all model versions for a given model, including the model version, model version ARN, and status. To list a subset of versions, use the MaxModelVersion and MinModelVersion fields.

listRetrainingSchedulers(params = {}, callback) ⇒ AWS.Request

Lists all retraining schedulers in your account, filtering by model name prefix and status.

listSensorStatistics(params = {}, callback) ⇒ AWS.Request

Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset. Can also be used to retreive Sensor Statistics for a previous ingestion job.

listTagsForResource(params = {}, callback) ⇒ AWS.Request

Lists all the tags for a specified resource, including key and value.

putResourcePolicy(params = {}, callback) ⇒ AWS.Request

Creates a resource control policy for a given resource.

startDataIngestionJob(params = {}, callback) ⇒ AWS.Request

Starts a data ingestion job. Amazon Lookout for Equipment returns the job status.

startInferenceScheduler(params = {}, callback) ⇒ AWS.Request

Starts an inference scheduler.

startRetrainingScheduler(params = {}, callback) ⇒ AWS.Request

Starts a retraining scheduler.

stopInferenceScheduler(params = {}, callback) ⇒ AWS.Request

Stops an inference scheduler.

stopRetrainingScheduler(params = {}, callback) ⇒ AWS.Request

Stops a retraining scheduler.

tagResource(params = {}, callback) ⇒ AWS.Request

Associates a given tag to a resource in your account. A tag is a key-value pair which can be added to an Amazon Lookout for Equipment resource as metadata. Tags can be used for organizing your resources as well as helping you to search and filter by tag. Multiple tags can be added to a resource, either when you create it, or later. Up to 50 tags can be associated with each resource.

untagResource(params = {}, callback) ⇒ AWS.Request

Removes a specific tag from a given resource. The tag is specified by its key.

updateActiveModelVersion(params = {}, callback) ⇒ AWS.Request

Sets the active model version for a given machine learning model.

updateInferenceScheduler(params = {}, callback) ⇒ AWS.Request

Updates an inference scheduler.

updateLabelGroup(params = {}, callback) ⇒ AWS.Request

updateModel(params = {}, callback) ⇒ AWS.Request

Updates a model in the account.

updateRetrainingScheduler(params = {}, callback) ⇒ AWS.Request

Updates a retraining scheduler.