What does it take to turn an AI idea into a real, scalable application without hiding the infrastructure behind a black box?

We'll start with an open source GitHub repository and use GitHub Copilot to understand, adapt, and operate a complete AI workload. We deploy Ray and KubeRay on Azure Kubernetes Service (AKS), run LLM batch inference on an NVIDIA GPU, and explore how Azure Linux provides a lightweight, secure container host for the Kubernetes nodes under the hood.

Along the way, we'll connect the layers that make the application work using containers, Kubernetes resources, GPU scheduling, and open source operators.

We'll also explore autoscaling, network isolation, authentication, metrics, dashboards, and cost-aware management. Leave with a reusable GitHub repo and a clear roadmap for building open source AI systems on Azure.