
Technology
Getting a stalled ML team from notebooks to weekly releases
Challenge
A data science team had working models stuck in notebooks - every release meant a manual handoff to engineering, and nobody could say with confidence what was actually running in production.
Strategy
Stand up a model registry and reproducible training pipeline first, then add automated evaluation gates so a release could ship without a manual sign-off meeting.
Execution
Built the registry, CI/CD evaluation gates, and drift monitoring around the team's existing models - no rewrite, no pause in shipping while the platform went in.
Outcome
Release cadence went from quarterly to weekly inside the first two months, with drift caught automatically instead of discovered by a confused user.
Six hours after go-live, the platform caught a drift the old process would have taken three weeks to notice.



