Introduction

ML model performance degrades over time as market distributions shift (concept drift, data drift). Statistical process control (SPC) charts—monitoring charts used in manufacturing quality control—detect model drift by tracking prediction error, model inputs, and prediction distributions over time. Detecting drift early enables model retraining before losses mount.

SPC Charting Techniques

Track key metrics over time: prediction error distributions, input variable distributions, prediction-outcome correlations. Plot moving averages and control limits (typically ±2-3 standard deviations from baseline). When metrics move outside control limits, drift is detected. Automate monitoring; alert operators when drift detected.

Drift Response

Drift detection triggers retraining: retrain model on recent data, validate performance on held-out test set. If improved, deploy retrained model. If not improved, investigate root cause (regime shift, data quality issue). Continuous monitoring and retraining maintains model freshness.

Conclusion

SPC-based drift monitoring enables proactive model maintenance, reducing performance degradation from market regime shifts.