Amazon Lookout for Equipment uses industrial data from your equipment to detect potential failures so you can take action, such as performing maintenance before a breakdown, to avoid unplanned downtime.
Amazon Lookout for Equipment demo
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.
Getting started sample
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.
Amazon CloudWatch Custom Widgets dashboard
Do you need non-developers to review your model and inference results? Don’t have the time to build your own monitoring applications? 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.
GitHub | Documentation
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