Detecting data and concept drift in production ML, plus the River online learning library

goyalshaliniuk · x · 2026-11-13

Part 2 of a thread on continuously learning AI systems covers data and concept drift: user behavior evolves, market conditions shift, new patterns appear in incoming data, and once-reliable predictions degrade.

Mitigations: monitor incoming data distributions, track prediction performance, and apply drift detection methods to know when adaptation is needed.

The author recommends River, a Python online machine learning library (6.1k GitHub stars) for streaming data, incremental learning and concept drift, with Rust components inside.

Original post →

Detecting data and concept drift in production ML, plus the River online learning library

More from Research