In this session, we’re going to dive into the exciting world of running AI/ML workloads on Google Kubernetes Engine (GKE)—and trust me, it's not as complex as it sounds! Whether you're new to Kubernetes or just curious about how AI models can be deployed in the cloud, this session is designed to give you a hands-on, beginner-friendly introduction to both.

We’ll start by breaking down the basics of Kubernetes, giving you a clear understanding of what it is and how it works. Think of Kubernetes as the ultimate conductor for your cloud infrastructure, orchestrating all the moving parts so your AI workloads can perform flawlessly.

From there, we'll get practical by containerizing a simple AI/ML model—yup, we’re putting that model into a neat little box (or rather, a Docker container) so it can be deployed and run efficiently in a GKE cluster. Don’t worry if that sounds a bit intimidating; we’ll guide you through the steps, making sure everything’s easy to follow and fun to do.

Finally, we’ll bring it all together by deploying our AI model on GKE, showing you how Kubernetes can handle these workloads at scale, with minimal fuss. By the end, you’ll walk away with foundational knowledge of running AI workloads in the cloud, and who knows, maybe you'll be inspired to deploy your own AI projects in no time!