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 ...
E166 - AI Agents at the Government Offices of Sweden - Magnus Enzell & Peter Nordström
AIAW Podcast
2 hours 17 minutes
1 month ago
E166 - AI Agents at the Government Offices of Sweden - Magnus Enzell & Peter Nordström
In Episode 166 of the AIAW Podcast, we sit down with Magnus Enzell and Peter Nordström from the Government Offices of Sweden to explore one of the most forward-thinking public sector AI initiatives in Europe. Together, they’ve helped launch over 30 AI agents inside Sweden’s central government—digital assistants designed to support civil servants with document handling, data processing, and smarter decision-making. We discuss the real-world impact of these agents, the lessons learned from depl...
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 ...
