View Source aws_lookoutequipment (aws v1.0.4)
Summary
Functions
Creates a container for a collection of data being ingested for analysis.
Creates a scheduled inference.
Creates a machine learning model for data inference.
Deletes a dataset and associated artifacts.
Deletes an inference scheduler that has been set up.
Deletes a machine learning model currently available for Amazon Lookout for Equipment.
Deletes a retraining scheduler from a model.
Generates a list of all model versions for a given model, including the model version, model version ARN, and status.
Lists statistics about the data collected for each of the sensors that have been successfully ingested in the particular dataset.
Starts a data ingestion job.
Associates a given tag to a resource in your account.
Removes a specific tag from a given resource.
Functions
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.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.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.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.Deletes an inference scheduler that has been set up.
Prior inference results will not be deleted.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.Deletes a retraining scheduler from a model.
The retraining scheduler must be in theSTOPPED status.
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 theMaxModelVersion and MinModelVersion fields.
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.Starts a data ingestion job.
Amazon Lookout for Equipment returns the job status.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.Removes a specific tag from a given resource.
The tag is specified by its key.