Architecting AI platforms for foundation model scale

Many organizations are reaching a turning point in their AI journey: API costs are rising, in-house GPU investments are growing, and teams are being asked to run more AI workloads inside their own infrastructure.

While Kubernetes and its developer ecosystem are the de facto foundation for modern platforms, this only addresses one piece of the AI platform challenge. AI workloads introduce new requirements around distributed computing, resource & dependency management, and developer productivity that existing platform tooling wasn't designed to solve.

You'll learn why platform teams at the world’s leading AI companies like Cursor, Uber, Netflix, and NVIDIA choose Ray to solve these problems. We’ll cover the evolving architecture of these platforms and what’s required to meet the scaling needs of AI today and tomorrow.