Building scalable, production-ready AI platforms is challenging, requiring robust infrastructure automation and model lifecycle management. This talk presents a conceptual architecture and practical blueprint for a cloud-native AI platform. We demonstrate how to build it using Kubernetes, open-source tools, and GitOps, focusing on design principles that ensure scalability, automation, and reproducibility. The aim is to create a highly automated, repeatable, production-ready environment for machine learning and AI workloads.

Target Audience: Architects, Developers, Platform Engineers, Data Scientists
Prerequisites:Basic knowledge in Kubernetes and platform engineering
Level: Practicing

Extended Abstract:
Developing scalable and production-ready AI platforms presents significant hurdles, encompassing not only architectural design but also critical aspects like infrastructure automation, robust orchestration, seamless model deployment, and comprehensive lifecycle management. Addressing these efficiently is paramount for organizational success in AI adoption.

This talk will delve into a conceptual architecture and practical blueprint for a cloud-native AI platform, emphasizing how Kubernetes, open-source technologies, and GitOps provide a powerful foundation. We will outline key design principles and best practices crucial for achieving scalability, automation, and reproducibility across the platform. A significant portion will be dedicated to demonstrating a step-by-step construction of this platform, both in local environments and within public cloud infrastructures. The core objective is to showcase how to establish a highly automated, repeatable, and production-ready environment specifically tailored for diverse machine learning and artificial intelligence workloads, thereby streamlining the entire AI development and deployment pipeline.