DEMO: ML Eval Checklist in 15 Minutes

DEMO CONTENT — swap this out with a real post later.

Quick-start checklist (placeholder):

1. Define target metric + slice metrics (e.g., accuracy + per-language).
2. Build a tiny, labeled eval set (50–100 rows) with edge cases.
3. Automate eval run in CI; fail on regression beyond threshold.
4. Log predictions + errors to a dashboard for triage.
5. Add a smoke test endpoint to ping the model after deploy.
6. Repeat weekly; grow the eval set from real incidents.