Feature engineering revisits in production typically occur on a quarterly or bi-annual basis for most stable models, though high-frequency trading or real-time recommendation systems may require monthly adjustments. The key is establishing automated monitoring rather than relying on fixed schedules. Primary indicators include statistical drift metrics like Population Stability Index (PSI) and Kullback-Leibler divergence to detect feature distribution changes, alongside performance degradation signals such as declining precision, recall, or business KPIs. Model prediction confidence scores dropping below historical thresholds often signal the need for feature updates. Effective monitoring strategies involve setting up dashboards that track feature statistics over time, implementing automated alerts when drift exceeds predefined thresholds, and maintaining shadow models with different feature sets to compare performance. Many teams also use techniques like adversarial validation to detect when new data significantly differs from training distributions, triggering feature engineering reviews before performance degrades noticeably.

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Revisit feature engineering when data drift impacts performance, typically every 3–6 months (or sooner if metrics drop).

Key indicators:

• Model performance decay (e.g., dropping accuracy/F1 score).

• Statistical drift (KS test, PCA, or feature distribution shifts).

• Domain shifts (e.g., policy changes, new user behavior).

Monitoring: Track input feature stats (mean, variance) and set alerts for anomalies. Retrain if drift exceeds thresholds.

Rule: Update features only if drift harms results—don’t fix what isn’t broken.