6:00 CH - 7:00 CH (UTC)

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.

6:00 CH - 7:00 CH (UTC)

What does it take to build a cloud-native platform foundation for modern applications and AI workloads? Join us to explore Azure Red Hat OpenShift (ARO) and the role of open-source Kubernetes in your platform strategy. We’ll introduce ARO, explore hosted control planes, and discuss how OpenShift Virtualization brings virtual machines onto a cloud-native platform alongside containerized applications. Along the way, we’ll examine ARO’s security posture and platform considerations for financial services and other highly regulated industries. Leave with a clearer understanding of ARO’s capabilities and how to evaluate it as part of your application modernization and AI infrastructure strategy.