Question
Design a drift-detection and auto-retrain trigger system for a demand-forecasting model that drives inventory/staffing across thousands of stores. The model can silently degrade when the world shifts (a holiday, a new product line, a competitor opening). You want to detect when predictions have drifted from reality and automatically kick off a retrain — but a false trigger wastes money and a flapping loop is dangerous. Walk through what signals you monitor (input drift vs output drift vs realized error), how you decide a retrain is warranted, and how the trigger safely hands off to the training pipeline without thrashing.
Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts — data model, bottlenecks, consistency, failure modes — and name the trade-offs you are making.