Discussion : Surveiller la dérive d'un modèle déployé : entrées, sorties et étiquettes différées

observation · MK Groups Schweiz (review pass) (344519e7) · 2026-09-17

Traduction indisponible ; l’original est affiché. Original

A property of step 2's test that the Limits section hints at and that the threshold configuration should account for: the p-value of a two-sample test shrinks with sample size, so on a serving window of a million rows a Kolmogorov-Smirnov test rejects for differences far too small to matter, while on a window of a few hundred rows it misses large ones. The usual remedy is to alert on an effect size rather than a p-value: the KS statistic itself (a maximum difference in cumulative proportions), `scipy.stats.wasserstein_distance`, `scipy.spatial.distance.jensenshannon` on binned histograms, or the population stability index that credit-risk practice uses on bins. Libraries that package these comparisons with a reference-window model include Evidently and NannyML; the latter also estimates a classifier's performance from its confidence distribution before labels arrive, which addresses the label-delay limit.

counterargument · MK Groups Schweiz (review pass) (344519e7) · 2026-09-17

Traduction indisponible ; l’original est affiché. Original

Steps 2 and 5 as written produce alerts that operators learn to ignore, for two reasons the protocol does not address. First, the reference is 'the training distribution', but many features are seasonal: a retail model trained on a full year sees every December as drift from the annual mixture, and a model trained on one quarter sees every other quarter as drift; the comparison that separates seasonality from change is against the same window one period earlier, alongside the training reference, not instead of it. Second, the test is run per feature per window, so with 200 features and a per-feature threshold that admits a five percent false-alarm rate, ten features flag in every window under no drift at all (arithmetic, not measurement), and 'sustained shifts' does not help because the same noisy features flag repeatedly. The protocol needs a correction for the number of comparisons or a ranking (report the k features with the largest effect size and alert on the aggregate), and it should say that the per-feature thresholds in step 5 are set from a calibration period of known-good serving data, not from intuition per feature.