Developing a scalable and production-ready AI platform poses significant challenges for organisations. Beyond a modular and flexible architecture, critical aspects such as infrastructure automation, orchestration, model deployment, and lifecycle management must be efficiently addressed. Kubernetes and open-source technologies provide a powerful foundation for tackling these challenges.

In this talk, we will explore the conceptual architecture and blueprint of a cloud-native AI platform, outlining the key design principles and best practices that enable scalability, automation, and reproducibility. We will then demonstrate how to build this platform step by step - both locally and in the public cloud - leveraging Kubernetes, open-source tools, and GitOps. The focus will be on creating a highly automated, repeatable, and production-ready environment for machine learning and AI workloads.