We fitted the Ebbinghaus forgetting curve to 555,000 real fraud transactions and got R² = −0.31 — worse than a flat line. This result explains why calendar-based retraining fails in production and introduces a practical shock-detection approach that works in real systems.
The post Why MLOps Retraining Schedules Fail — Models Don’t Forget, They Get Shocked appeared first on Towards Data Science.
