Playbook
We have 30 machine-learning models in production and no idea which ones are drifting
A bank's machine-learning team has deployed dozens of models with no shared discipline. There's no central record of what's running, retraining happens by accident, and drift detection lives in one person's notebook. The regulator has asked how the bank knows the models are still fit for purpose.
Trigger — Regulator review; the bank can't show how it knows the models still work.
Good outcome — The bank has a single register of every model in production, can show which ones are drifting, and retrains on a planned schedule. The regulator gets a clear answer about model oversight.