Model Drift Monitoring
Model Drift Monitoring — Detecting distribution shift that breaks model performance.
Definition
Model Drift Monitoring refers to detecting distribution shift that breaks model performance. Keep that definition fixed when comparing series, managers, or regimes — renaming the same tape does not create a new signal.
Why it matters
It binds model output to retrieval, tools, or evaluation so answers stay grounded instead of free-floating. When detecting distribution shift that breaks model performance shifts, related hedges, limits, and narratives usually need an explicit update rather than a quiet assumption.
Case
Suppose a desk is positioned for the opposite of what model drift monitoring is saying. If detecting distribution shift that breaks model performance moves against that book, the first question is not “is the story clever?” but whether size, hedges, and stop logic still match the observation.
How to read it
Measure grounding rate, latency, and failure modes under missing context — not demo chat quality alone. Prefer a short written null hypothesis for Model Drift Monitoring: what would falsify the current reading in the next window?