Amazon Lookout for Equipment Resources

The user guide provides a conceptual overview of Amazon Lookout for Equipment, offers detailed instructions for using various features, and includes a complete API reference for developers.</p><p><a href="https://docs.aws.amazon.com/lookout-for-equipment/latest/ug/amazon-lookout-for-equipment-ug.pdf">PDF</a></p>

This getting started sample can be used to develop hands-on experience on how to query the Amazon Lookout for Equipment API. Use this content to get started with ingesting data, building models, and building nice visualizations to review your model results.

This getting started focuses solely on the Lookout for Equipment console. This will walk you through an end-to-end journey using Lookout for Equipment from ingesting the data to deploying an anomaly detection model. All of this, without writing a single line of code!

Do you need non-developers to review your model and inference results? What about using CloudWatch custom widgets to build beautiful dashboards for any internal user? This repo comes with a CloudFormation template you just need to deploy before following this Read Me note.

Dedicated to Python developers, the Amazon Lookout for Equipment Python SDK is an open-source library that allows data scientists and software developers to build, train, and deploy anomaly detection models for industrial time-series data using Amazon Lookout for Equipment. This library, available from the PyPI package repository, allows you to ingest data, train models, and display beautiful visualization you can incorporate with a few Python lines of code. Read the full documentation and check out a short example illustrated in a concise notebook.

The AWS Samples GitHub repository for Amazon Lookout for Equipment contains examples illustrating how you can start building your anomaly detection application for your industrial assets. The examples are in Jupyter notebooks and contain detailed commentaries and explanations, making it easy to understand how to use the service. They include step-by-step guidance for configuring the Boto3 API for Lookout for Equipment and invoking various APIs for preparing the data, creating a dataset, training a model, evaluating a model, and scheduling an inferencer. You can also find helper functions for visualizing your time-series data.

Lookout for Equipment Installation Instructions The Lookout for Equipment Web App is an application you can deploy in your own account with a simple CloudFormation template. This application will help you assess quickly if your industrial data contains any historical anomaly of interest. After you upload a dataset containing sensor measurements from your industrial equipment or manufacturing process, the app will guide you to train an anomaly detection model. You will then be able to visualize your model results and deploy it, making it ready to receive your shop floor live data. This application also includes an operational dashboard to visualize your live results.

by Harunobu Kameda Dec 1, 2020

by Julia Hu, Dastan Aitzhanov, Michaël Hoarau, and Sebastian Salomon Dec 16, 2021

by Julia Hu, Theodore Bakanas, Dastan Aitzhanov, and Michaël Hoarau Dec 16, 2021

by Johann Fuechsl and Michaël Hoarau Mar 28, 2023

by Michaël Hoarau, Moulham Zahabi, and Fauzank Apr 12, 2023