shadow deployment

Shadow deployment runs a new model alongside production without affecting users, validating predictions in real conditions. **How it works**: Production traffic duplicated to shadow model. Shadow predictions logged but not served. Compare predictions to production model and ground truth. **Purpose**: Validate new model on real traffic patterns before promoting. Catch issues without user impact. **What to evaluate**: Prediction agreement with production, latency performance, resource usage, edge case handling, error rates. **Comparison methods**: Log both predictions, compare offline. Statistical analysis of disagreements. Manual review of interesting cases. **Duration**: Run until confident - typically days to weeks depending on traffic volume and variability. **Infrastructure needs**: Duplicate inference pipeline, logging infrastructure, comparison tooling, minimal latency impact on production. **When to use**: High-risk model changes, new architectures, major retraining, regulated environments. **Progression**: Shadow mode, then canary deployment, then full rollout. Risk mitigation. **Limitations**: Does not catch issues from actually serving predictions (feedback loops, user behavior changes).