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 ...
E155 - The Dawn of Agentic AI: From Theoretical Concepts to Practical Implementation
AIAW Podcast
2 hours 32 minutes
7 months ago
E155 - The Dawn of Agentic AI: From Theoretical Concepts to Practical Implementation
Get ready for a deep dive into the future of AI with Episode 155 of the AIAW Podcast! Join us as four industry experts —Luka Crnkovic-Friis (King / Microsoft), Martin Lundqvist (Arundo Analytics), Vilhelm von Ehrenheim (QA.tech), and Jesper Fredriksson (Volvo Cars)—unpack Agentic AI: From Practical Implementation to Strategic Implications. Discover how agentic AI’s autonomy, adaptability, and goal-driven intelligence reshape industries, from single-agent workflows to multi-agent ecosystems. W...
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 ...
