Don’t Retrain the Whole Map When One Neighborhood Moves
TL;DR for operators Most production model-maintenance policies sit between two unattractive extremes: never retrain and watch performance decay, or retrain constantly and turn the MLOps budget into a recurring tribute payment. This paper tests a third option.1 It divides the feature space into clusters, maintains a separate ADWIN error detector for each cluster, and triggers model adaptation when one of those regional error streams changes significantly. The goal is not to beat continuous retraining at any cost. It is to approach its predictive performance while avoiding its habit of rebuilding the model whenever another batch enters the building. ...