Scaling Multimodal Data and Reinforcement Learning with Ray
AI Infrastructure, OSS AI
AI workloads are growing in complexity and require increasing scale of both data and compute, as well as significant heterogeneity across models, data types, and hardware accelerators.
As a result, the software stack for running compute-intensive AI workloads is fragmented and rapidly evolving. However, within the fragmented landscape, common patterns are beginning to emerge.
This talk describes a popular software stack combining Kubernetes, Ray, PyTorch, and vLLM designed to support a variety of emerging AI workloads including multimodal data processing and reinforcement learning. It describes the role of each of these frameworks and how they operate together.
Jaikumar Ganesh is an accomplished technology leader and the Head of Engineering at Anyscale.
With a deep background in engineering and customer-facing roles, Jaikumar has a proven track record of building and scaling engineering organizations.
He is passionate about pushing the boundaries of product and engineering innovation while ensuring customer needs are met, and is committed to building empowering organizations rooted in trust, respect, and growth.
Jaikumar is excited about working with companies to harness the power of AI and distributed computing to achieve their goals. He previously co-started and co-led Uber's AI group—the central ML group at Uber—and was also on the early team at Android @ Google.