Safety
93d agoDetecting Drift in ML Systems
Real-world harms often stem from undetected drift in machine learning systems. Researchers at Carnegie Mellon University detail the specific triggers of this decay and how to monitor for it. Practitioners must implement these detection measures to prevent model failure.
The Signal
This guide provides a baseline for maintaining long-term system reliability and safety.