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 ...
E162 - Bridging US-SWE AI Ecosystems - Minna Sandberg
AIAW Podcast
2 hours 6 minutes
2 months ago
E162 - Bridging US-SWE AI Ecosystems - Minna Sandberg
In Episode 162 of the AIAW Podcast, we sit down with Minna Sandberg, Founder & CEO of Swenode.ai, for a deep-dive into how Sweden can strengthen its position in applied AI by building meaningful bridges with Silicon Valley. We explore the mission behind Swenode.ai and its role in enabling collaboration between Swedish startups, industry leaders, and the global tech ecosystem. Minna shares reflections on the cultural and structural contrasts between Sweden and the U.S. when it comes to AI ...
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 ...
