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.
Keywords platform, productivity, kubernetes, open source, ai
https://github.com/lreimer/k3s-ai-platform
A tiny AI platform that is suitable to run locally on RD, k3s, Kind et.al
https://github.com/lreimer/k8s-native-ai-platform
Demo repository for a Kubernetes-native AI platform.