00:00:00Main Episode
00:00:22Opening
00:01:13Musical break
SOBRE ESTE EPISÓDIO
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.
Inglês
Estados Unidos da América
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OUTROS EPISÓDIOS NESTE PODCAST
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In this episode of YAAP, Yuval talks with Renee Lau from AWS, a financial services industry specialist who works hands on with banks, insurers, and hedge funds as they try to move generative AI from pilots into production. Renee shares what she s…
Leaderboards reward “best average score.”
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In this special deep dive episode, AI21’s CTO Barak Lenz walks through four gaps between what models can do and what real AI systems deliver: validation, contextualization (pick the…
Running multiple agents can improve quality. Doing it right is the hard part.
This time we look at the Agent Swarm Fallacy: the idea that throwing more agents at a problem automatically makes systems better. Yuval sits with Or Dagan, AI21 CPO, to explore why this breaks in practice, what happens …
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