Most production models are static. The world keeps moving, data distributions shift, and the system you deployed starts quietly degrading. Full retraining is expensive and slow. Doing nothing is quietly catastrophic. The teams who've solved this are using continual learning strategies that keep models current without starting from zero every time. This session covers the practical approaches engineering teams are using to handle model drift, incorporate new data, and maintain performance over time. Key takeaways: →How to detect model drift before it surfaces as visible degradation in production → The continual learning strategies that don't require full retraining cycles →How to structure your data pipeline to support ongoing model updates at reasonable cost