Containerizing an AI model and deploying it to Amazon Elastic Kubernetes Service (Amazon EKS) is a significant milestone. Your Helm charts apply cleanly, your NVIDIA GPU worker nodes are provisioned, and your inference pods report a Running status. However, in enterprise machine learning, deployment is only 20% of the operational lifecycle. The remaining 80% is the hard engineering reality of Day-2 operations: keeping high-throughput, non-deterministic AI models performant, r