amazonaws.com – lookoutequipment

Tools (35)

Extracted live via the executor SDK.

• xAmzTargetAwsLookoutEquipmentFrontendServiceCreateDataset.createDataset

  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. In other words, 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.

• xAmzTargetAwsLookoutEquipmentFrontendServiceCreateInferenceScheduler.createInferenceScheduler

  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.

• xAmzTargetAwsLookoutEquipmentFrontendServiceCreateLabel.createLabel

  Creates a label for an event.

• xAmzTargetAwsLookoutEquipmentFrontendServiceCreateLabelGroup.createLabelGroup

  Creates a group of labels.

• xAmzTargetAwsLookoutEquipmentFrontendServiceCreateModel.createModel

  Creates an ML 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.

• xAmzTargetAwsLookoutEquipmentFrontendServiceDeleteDataset.deleteDataset

  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.

• xAmzTargetAwsLookoutEquipmentFrontendServiceDeleteInferenceScheduler.deleteInferenceScheduler

  Deletes an inference scheduler that has been set up. Already processed output results are not affected.

• xAmzTargetAwsLookoutEquipmentFrontendServiceDeleteLabel.deleteLabel

  Deletes a label.

• xAmzTargetAwsLookoutEquipmentFrontendServiceDeleteLabelGroup.deleteLabelGroup

  Deletes a group of labels.

• xAmzTargetAwsLookoutEquipmentFrontendServiceDeleteModel.deleteModel

  Deletes an ML 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.

• xAmzTargetAwsLookoutEquipmentFrontendServiceDescribeDataIngestionJob.describeDataIngestionJob

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceDescribeDataset.describeDataset

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceDescribeInferenceScheduler.describeInferenceScheduler

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceDescribeLabel.describeLabel

  Returns the name of the label.

• xAmzTargetAwsLookoutEquipmentFrontendServiceDescribeLabelGroup.describeLabelGroup

  Returns information about the label group.

• xAmzTargetAwsLookoutEquipmentFrontendServiceDescribeModel.describeModel

  Provides a JSON containing the overall information about a specific ML model, including model name and ARN, dataset, training and evaluation information, status, and so on.

• xAmzTargetAwsLookoutEquipmentFrontendServiceListDataIngestionJobs.listDataIngestionJobs

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceListDatasets.listDatasets

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceListInferenceEvents.listInferenceEvents

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceListInferenceExecutions.listInferenceExecutions

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceListInferenceSchedulers.listInferenceSchedulers

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceListLabelGroups.listLabelGroups

  Returns a list of the label groups.

• xAmzTargetAwsLookoutEquipmentFrontendServiceListLabels.listLabels

  Provides a list of labels.

• xAmzTargetAwsLookoutEquipmentFrontendServiceListModels.listModels

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceListSensorStatistics.listSensorStatistics

  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.

• xAmzTargetAwsLookoutEquipmentFrontendServiceListTagsForResource.listTagsForResource

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceStartDataIngestionJob.startDataIngestionJob

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceStartInferenceScheduler.startInferenceScheduler

  Starts an inference scheduler.

• xAmzTargetAwsLookoutEquipmentFrontendServiceStopInferenceScheduler.stopInferenceScheduler

  Stops an inference scheduler.

• xAmzTargetAwsLookoutEquipmentFrontendServiceTagResource.tagResource

  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.

• xAmzTargetAwsLookoutEquipmentFrontendServiceUntagResource.untagResource

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

• xAmzTargetAwsLookoutEquipmentFrontendServiceUpdateInferenceScheduler.updateInferenceScheduler

  Updates an inference scheduler.

• xAmzTargetAwsLookoutEquipmentFrontendServiceUpdateLabelGroup.updateLabelGroup

  Updates the label group.

• openapi.previewSpec

  Preview an OpenAPI document before adding it as a source

• openapi.addSource

  Add an OpenAPI source and register its operations as tools