As AI/ML workloads scale in cloud-native environments, ensuring the security, efficiency, and reliability of Kubernetes-based deployments becomes critical. This talk will explore the common challenges faced when building and managing container images for AI/ML workloads—ranging from dependency management and compatibility issues to the security risks posed by bloated images and CVEs.

We’ll dive into the challenges we faced while optimizing our container images, focusing on reducing size, achieving zero CVEs, and overcoming testing and performance hurdles. By discussing proven techniques like minimizing unnecessary dependencies, selecting secure base images, and optimizing build times, we’ll provide actionable insights and a clear roadmap for streamlining and securing AI/ML workflows at scale.