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Handling Concept Drift in Live ML Systems
When the relationship a model learned changes, retraining is the usual answer, but how you retrain matters more than how often. On real bike-sharing data where demand grew by two-thirds under the same conditions, monthly retraining on all history barely helped; a sliding window or one feature halved the error.
Model Drift and Data Drift: Detection and Response
Most drift tooling watches a model's inputs. This guide shows, on three Januaries of real New York taxi trips, a change that left every input statistic flat while the model's error grew fivefold, why significance tests mislead at scale, and how to respond to drift you detect and to change you know is coming.