anomaly-detection

Introduction

It is a reference implementation of intelligent monitoring which can utilize SODA Telemetry data, through exporters with Kafka (or similar) and a standard ML algorithm.

Any machine learning algorithm based on the use case can be integrated to this framework for specific data analysis and prediction.

This is one of the SODA Core Projects and is maintained by SODA Foundation directly. However there can be other SODA Compliant Anomaly Detection or intelligent telemetry projects from partners in future which can be part of SODA Foundation Project Landscape.

Documentation

https://docs.sodafoundation.io

Quick Start - To Use/Experience

https://docs.sodafoundation.io

Quick Start - To Develop

https://docs.sodafoundation.io

Latest Releases

https://github.com/sodafoundation/anomaly-detection/releases

Support and Issues

https://github.com/sodafoundation/anomaly-detection/issues

Project Community

https://sodafoundation.io/slack/

How to contribute to this project?

Join https://sodafoundation.io/slack/ and share your interest in the ‘general’ channel

Checkout https://github.com/sodafoundation/anomaly-detection/issues labelled with ‘good first issue’ or ‘help needed’ or ‘help wanted’ or ‘StartMyContribution’ or ‘SMC’

Project Roadmap

Provide advanced anomaly detection and prediction based on the resource or performance data.

https://docs.sodafoundation.io

Join SODA Foundation

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