You wanted to build an agent.

You ended up debugging GPUs, scaling workers, and chasing OOMs.

In this episode of YAAP, Yuval sits down with Linda from Anyscale to unpack why Ray exists and how it helps AI teams scale without turning every developer into a distributed systems expert.

We trace Ray’s roots in reinforcement learning research, then zoom out to how it’s used today across the AI pipeline: data processing, training, inference, and agents. Along the way, we cover why libraries like vLLM build on Ray, when Ray vs. SaaS makes sense, and why unstructured and multimodal data push traditional big-data tools to their limits.