A model that tests well offline may still behave differently in production. Gradual rollout strategies reduce risk.
Shadow Deployment
The new model receives copies of live requests and makes predictions, but its outputs aren't used. Compare them with the current model's predictions and later outcomes.
- No user impact.
- Reveals performance on real data and operational issues like latency.
Canary Release
Route a small share of traffic to the new model, monitor closely, and increase gradually if metrics hold.
A/B Testing
Randomly split traffic between models to measure business impact with statistical rigour.
Blue-Green Deployment
Run old and new versions side by side and switch traffic at once, with instant rollback.
What to Monitor
- Prediction distributions.
- Latency and errors.
- Business metrics and outcomes.
Rollback
Always have a quick, tested way back to the previous model.