essay
Closing the Loop on ML Evals
A practical framework for turning evaluation insights into deployable improvements.
Reliable ML systems rarely fail in the obvious ways. They degrade silently, drift, or encounter input regimes we did not plan for.
A simple loop
- Define a real-world slice you care about.
- Measure it daily with a lightweight probe set.
- Convert regressions into training or routing fixes.
A few wins
- Write tests for your data, not just your code.
- Trend evaluation metrics, not just aggregate them.
- Treat eval failures as backlog items.