What strategies did you implement to ensure that your deployed models performed well and to quickly identify and resolve any issues that arose?

We adopt a blue‑green deployment pipeline: the new model version is deployed to a separate environment, and traffic is gradually shifted using a feature flag. We monitor key metrics—latency, error rate, and accuracy—through a real‑time dashboard powered by Grafana and Prometheus. If any metric deviates beyond a predefined threshold, an automated rollback is triggered. We also use A/B testing to compare the new model against the baseline on a small traffic slice, collecting statistical evidence before full rollout. Additionally, we instrument the inference code to log request metadata and prediction confidence, feeding this data into an ML‑ops pipeline that retrains the model offline if drift is detected. Finally, we maintain a versioned model registry and use containerization (Docker) to guarantee reproducibility. This approach allows us to quickly identify performance regressions, roll back safely, and continuously improve the model with minimal impact on users.