Shadow mode runs a candidate model alongside the current model while keeping its predictions out of user-facing decisions. It provides real-world evidence without risking production outcomes. section 13.5.1
Use shadow mode as a controlled release checkpoint:
- Complete rigorous offline validation before parallel production testing. section 13.5.1
- Compare candidate and current-model performance under real-world traffic without applying candidate predictions. section 13.5.1
- Check monitoring signals, including system health, response times, error rates, model metrics, and data drift. section 13.5.1
- Confirm preprocessing, feature engineering, and dependent pipelines match the candidate model before rollout. section 13.5.1
A safer model release
A retail team validates a new model offline, then runs it beside the current model. The team compares predictions and performance in production while users continue receiving decisions from the current model. section 13.5.1
