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 ...
E168 - AI Innovation at Central Bank of Sweden - Hugi Aegisberg
AIAW Podcast
2 hours 17 minutes
2 weeks ago
E168 - AI Innovation at Central Bank of Sweden - Hugi Aegisberg
In this episode of the AIAW Podcast, we’re joined by Hugi Aegisberg, AI & Innovation Lead at the Central Bank of Sweden (Sveriges Riksbank), for a forward-looking conversation on how artificial intelligence is reshaping the future of central banking. From rethinking monetary policy and financial stability to building public trust through open-source AI and digital assistants, Hugi shares practical insights from inside one of the world’s oldest financial institutions. We dive into Sw...
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 ...
