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 ...
E158 - Where AI Meets Intellectual Property - Rasmus Fredlund and Daniel Enetoft
AIAW Podcast
1 hour 56 minutes
6 months ago
E158 - Where AI Meets Intellectual Property - Rasmus Fredlund and Daniel Enetoft
Join us on AIAW Podcast Episode 158 as we delve into the complex world of AI and intellectual property with Rasmus Fredlund and Daniel Enetoft, European Patent Attorneys at EIP. From the challenges of patenting AI-generated inventions to navigating open-source strategies, the EU AI Act, and global regulatory contrasts, this episode is a must-listen for anyone at the crossroads of tech, law, and innovation. Discover how legal frameworks are evolving to meet the pace of AI and what it means for...
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 ...
