model retraining

Model retraining periodically updates model weights on fresh data to maintain performance as distributions shift. **Why retrain**: Combat data drift and concept drift, incorporate new patterns, improve on mistakes, adapt to changing world. **Retraining strategies**: **Scheduled**: Fixed intervals (daily, weekly, monthly). Simple but may miss urgent needs. **Triggered**: When performance degrades below threshold or drift detected. Responsive but complex. **Continuous**: Online learning with streaming data. Always current but harder to manage. **What to keep**: Architecture, hyperparameters (unless tuning), training pipeline. **What changes**: Training data (add recent, possibly remove old), weights. **Data windows**: Use all historical data, sliding window (last N months), weighted by recency, or combination. **Validation**: Always validate new model before deployment. A/B test or shadow mode. **Automation**: Automated retraining pipelines detect trigger, retrain, validate, deploy. Full MLOps. **Challenges**: Training compute costs, validation time, rollback planning, handling concept drift mid-training. **Best practice**: Monitor continuously, retrain proactively, validate thoroughly before promotion.