Amazon Lookout for Equipment Samples

Amazon Lookout for Equipment uses the data from your sensors to detect abnormal equipment behavior, so you can take action before machine failures occur and avoid unplanned downtime.

This repository contains notebooks and examples on how to onboard and use various features of Amazon Lookout for Equipment. At the moment, it only contains the getting started guides, but this repository will be soon populated with various content and samples that will help you better integrate the services:

. lookout-for-equipment
|
├── data/                                
|   # This directory will be generated when you will run the the
|   # different samples available in this repository.
|
├── apps/ (*NEW*)
|   # This direction contains apps showcasing how to integrate Amazon Lookout
|   # for Equipment insights into your own applications and business
|   # process.
|
├── blogs/ (*COMING SOON*)
|   # Technical content associated to blog posts AWS writes about
|   # Amazon Lookout for Equipment will be hosted here.
|
├── getting_started/
|   # These notebooks can be used to follow along the getting started 
|   # section of the documentation and will get you started with how to
|   # prepare your data to feed them to Amazon Lookout for Equipment.
|
├── integration
|   # You will find here some code snippets and templates showcasing
|   # how you can integrate with larger industrial ecosystems (connectivity
|   # to various data historians for instance).
|
├── model-evaluation
|   # These code snippets will show how to post-process your model results,
|   # how to monitor inferences and perform model continuous improvement.
|
├── model-training
|   # Training and improving models will be a key part of getting great
|   # insights your plants will be able to leverage to reinforce their
|   # maintenance practices.
|
├── preprocessing
|   # Multivariate industrial time series data can be challenging to deal
|   # with: these samples will show you how to explore your data, improve
|   # data quality, label your anomalies (manually or automatically), etc.
|
└── utils/
    |
    └── lookout_equipment_utils.py (*NEW*)

Getting started notebooks

This folder contains various examples covering Amazon Lookout for Equipment best practices. Open the getting_started folder to find all the ressources you need to train your first anomaly detection model. The notebooks provided can also serve as a template to build your own models with your own data.

In the getting_started folder, you will learn to:

1. Prepare your data for use with Amazon Lookout for Equipment
2. Create your own dataset
3. Train a model based on this dataset
4. Evaluate a model performance and get some diagnostics based on historical data
5. Build an inference scheduler and post-process the predictions
6. Clean the ressources created by Amazon Lookout for Equipment

Apps

This folder contains apps and solution you can deploy thanks to CloudFormation templates to accelerate your usage of Amazon Lookout for Equipment.

This folder currently contain a CloudFormation template to deploy a CloudWatch dashboarding suite with:

• A dashboard summarizing all the models trained in your account
• A dashboard with all the schedulers configured in your account
• The ability to create dedicated model and scheduler dashboards
• CloudWatch Synthetics Canaries to send dashboard snapshot to an email address

Security

See CONTRIBUTING for more information.

License

This collection of notebooks is licensed under the MIT-0 License. See the LICENSE file.