Empirical study compares retraining policies for streaming ML under concept drift, budget, and latency constraints
Read the original at arxiv.org→arXiv:2608.19488v1 Announce Type: new Abstract: Production machine learning systems degrade under concept drift, yet practitioners have little principled guidance on when to retrain. Retraining is costly, retraining...
Original headline: "When to Retrain: An Empirical Study of Retraining Policies for Streaming ML Under Concept Drift, Budget, and Latency Constraints"
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- Aug 21, 04:00 UTC arXiv cs.LG lead source When to Retrain: An Empirical Study of Retraining Policies for Streaming ML Under Concept Drift, Budget, and Latency Constraints