In Episode 170 of the AIAW Podcast, we’re joined by Jim Dowling, CEO of Hopsworks, co-creator of featurestore.org, and author of the upcoming O’Reilly book Building Machine Learning Systems with a Feature Store. Known as "Mr. Feature Store," Jim walks us through the evolution of AI infrastructure. From traditional batch learning to real-time, agentic workflows powered by vector databases, RAG, and LLMs. We discuss how feature stores serve as the memory layer of AI agents, enabling contextual ...
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In Episode 170 of the AIAW Podcast, we’re joined by Jim Dowling, CEO of Hopsworks, co-creator of featurestore.org, and author of the upcoming O’Reilly book Building Machine Learning Systems with a Feature Store. Known as "Mr. Feature Store," Jim walks us through the evolution of AI infrastructure. From traditional batch learning to real-time, agentic workflows powered by vector databases, RAG, and LLMs. We discuss how feature stores serve as the memory layer of AI agents, enabling contextual ...
E165 - How to Build Autonomous AI Agents - Henrik Kniberg
AIAW Podcast
2 hours 28 minutes
1 month ago
E165 - How to Build Autonomous AI Agents - Henrik Kniberg
In this Episode 165 of the AIAW Podcast, we are joined by Henrik Kniberg, an AI whisperer, Chief Scientist and Co-Founder of Abundly.ai, and author of Generative AI in a Nutshell, for a wide-ranging conversation about the transformative power of AI agents. With a background spanning Spotify, LEGO, and Mojang’s Minecraft, Henrik brings a rare blend of technical insight and human-centered thinking. We’ll explore his vision for Abundly.ai, how autonomous agents can boost productivity, crea...
AIAW Podcast
In Episode 170 of the AIAW Podcast, we’re joined by Jim Dowling, CEO of Hopsworks, co-creator of featurestore.org, and author of the upcoming O’Reilly book Building Machine Learning Systems with a Feature Store. Known as "Mr. Feature Store," Jim walks us through the evolution of AI infrastructure. From traditional batch learning to real-time, agentic workflows powered by vector databases, RAG, and LLMs. We discuss how feature stores serve as the memory layer of AI agents, enabling contextual ...
