From notebook to production for ML features
A short checklist for turning experiments into APIs you can ship and monitor.
Start by freezing dependencies and packaging inference behind a narrow API contract. Add health checks, timeouts, and basic load tests before you expose anything to users.
Logging inputs and outputs (with privacy in mind) makes debugging drift far easier than staring at aggregate metrics alone.