How often you update feature engineering after deployment to handle data drift in ML ?

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Ahmad

Updated on May 30, 2025 in

In your machine learning projects, once a model is deployed, how often do you revisit and adjust the feature engineering process to address issues caused by data drift?
What indicators or monitoring strategies help you decide when updates are needed?

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PangaeaX on May 30, 2025

This is amazing

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Miley on April 28, 2025

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.

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Revisit feature engineering when data drift impacts performance, typically every 3–6 months (or sooner if metrics drop). 
Key indicators: 
<ul> 
<li> 
Model performance decay (e.g., dropping accuracy/F1 score). 
</li> 
<li> 
Statistical drift (KS test, PCA, or feature distribution shifts). 
</li> 
<li> 
Domain shifts (e.g., policy changes, new user behavior). 
</li> 
</ul> 
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.

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