Give us a star ⭐️ on GitHub to support the project!
A comprehensive introduction to ML monitoring, including a framework to organize ML monitoring metrics and steps to take when dealing with data drift in production.
No ML model lasts forever. To operate it successfully, you need a real-time view of its performance. Does it work as expected? What is causing the change? Is it time to intervene? This sort of visibility is not a nice-to-have, but a critical part of the model development lifecycle. Here comes ML monitoring.
Download the Big Book of ML Monitoring to learn:
