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.


User guide

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.


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 model and building nice visualizations to review your model results.


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 leveraging 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.


Python SDK

Dedicated to Python developers, the Amazon Lookout for Equipment Python SDK is an open-source library that allows data scientists and software developers to easily 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 easily ingest data, train models and display beautiful visualization you can incorporate with a few Python lines of code. Read the full documentation here and check out a short example illustrated in a concise notebook here.

GitHub | Documentation


Analyze Existing Sensor Data to Detect Abnormal Equipment Behavior with Amazon Lookout for Equipment
AWS re:Invent 2020: Detect abnormal equipment behavior by analyzing sensor data (27:46)
AWS re:Invent 2020: AWS for Industrial with Matt Wood (15:27)


New – Amazon Lookout for Equipment Analyzes Sensor Data to Help Detect Equipment Failure
by Harunobu Kameda
Dec 1, 2020

Read blog »

Strengthening Operational Insights for Industrial Assets with AWS IoT AIML Solution (part 1)
by Julia Hu, Dastan Aitzhanov, Michaël Hoarau, and Sebastian Salomon
Dec 16, 2021

Read blog »

Strengthening Operational Insights for Industrial Assets with AWS IoT AIML Solution (part 2)
by Julia Hu, Theodore Bakanas, Dastan Aitzhanov, and Michaël Hoarau
Dec 16, 2021

Read blog »

Standard Product Icons (Features) Squid Ink
Check out the product features

Visit the Amazon Lookout for Equipment features page.

Learn more 
Sign up for a free account
Sign up for a free account

Instantly get access to the AWS Free Tier. 

Sign up 
Standard Product Icons (Start Building) Squid Ink
Start building in the console

Get started building with Amazon Lookout for Equipment in the AWS Management Console.

Sign in