Your question is Docker and Kubernetes ML Deployment. Take a moment with it on the right.
Talk me through your thinking if you like. When you're confident, submit your answer and I'll grade it like a real screen (7/10 or better passes).
Can you explain your experience with containerization using Docker and orchestration via Kubernetes?
Discuss how you package a trained traditional machine learning model, expose it for inference, and deploy it reliably in Kubernetes. Cover image construction, dependency management, configuration, health checks, resource limits, scaling, rollout strategy, and monitoring. Explain how you prevent training-serving inconsistencies and validate that the deployed model meets latency and prediction-quality requirements. Include practical Python code for an inference service and describe the Kubernetes resources you would use.