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• OpenSearch Anomaly Detection
• Highlights
• Documentation
• Contributing
• Code of Conduct
• Security
• Licensing
• Copyright

The OpenSearch Anomaly Detection plugin enables you to leverage Machine Learning based algorithms to automatically detect anomalies as your log data is ingested. Combined with OpenSearch Alerting, you can monitor your data in near real time and automatically send alert notifications . With an intuitive OpenSearch Dashboards interface, and a powerful API, it is easy to set up, tune, and monitor your anomaly detectors.

Anomaly detection is using the Random Cut Forest (RCF) algorithm for detecting anomalous data points.

Anomaly detections run a scheduled job using job-scheduler.

You should use anomaly detection plugin with the same version of OpenSearch Alerting. You can also create a monitor based on the anomaly detector. A scheduled monitor run checks the anomaly detection results regularly, and collects anomalies to trigger alerts based on custom trigger conditions.

Please see our documentation.

We welcome you to get involved in development, documentation, and testing of the anomaly detection plugin.

See our contribution guidelines and the developer guide to get started.

If you are looking for a quick contribution, we still don't have 100% unit test coverage for now. Check out GitHub issues for other ideas.

This project has adopted an Open Source Code of Conduct.

If you discover a potential security issue in this project we ask that you notify AWS/Amazon Security via our vulnerability reporting page. Please do not create a public GitHub issue.

See the LICENSE file for our project's licensing. We will ask you to confirm the licensing of your contribution.

Copyright 2021 OpenSearch Contributors