Could you discuss your experience with deploying AI models in a production environment? What tools and practices did you use to ensure smooth deployment and scaling?

I have deployed models in both on‑prem and cloud environments. The typical stack starts with Dockerizing the model and its dependencies, then orchestrating with Kubernetes and Helm charts. For model serving I use TorchServe or TensorFlow Serving, exposing a REST endpoint behind an Envoy sidecar for traffic shaping. I integrate MLflow for experiment tracking and model registry, which allows version promotion to production. Continuous delivery is handled with GitHub Actions that build the image, run unit tests, and push to a private registry. For scaling I rely on Kubernetes autoscaling (HPA) and use a GPU node pool for inference. Monitoring is done with Prometheus metrics (latency, error rate) and Grafana dashboards, while A/B testing and canary releases are managed via Istio. I also set up a rollback strategy that automatically reverts to the previous stable model if the new version exceeds a threshold of errors. This pipeline ensures that models can be deployed, monitored, and scaled with minimal manual intervention.