Why MLOps Retraining Schedules Fail — Models Don’t Forget, They Get Shocked

The traditional calendar-based retraining schedules for machine learning models often fail in production because they do not account for the actual concept drift in the data, which can be better explained by a shock-detection approach rather than the Ebbinghaus forgetting curve. By analyzing 555,000 real fraud transactions, the study found that the Ebbinghaus forgetting curve does not accurately model the performance degradation of machine learning models over time, with a poor R² value of -0.31

⚡ Key Takeaways

• The Ebbinghaus forgetting curve is not a suitable model for predicting the performance degradation of machine learning models over time.
• Calendar-based retraining schedules can fail in production due to their inability to account for concept drift in the data.
• A shock-detection approach can be a more effective method for determining when to retrain machine learning models.

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