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 ...
E167 - Practical Limitations for Agentic Business Processes - Luka Crnkovic-Friis
AIAW Podcast
2 hours 37 minutes
3 weeks ago
E167 - Practical Limitations for Agentic Business Processes - Luka Crnkovic-Friis
In this episode, Luka Crnkovic-Friis, Head of AI/ML at King (Microsoft), joins us for a sharp, honest look at the real-world challenges of deploying agentic AI systems at scale. Drawing from his experience leading AI innovation at the company behind Candy Crush, Luka unpacks the practical bottlenecks of integrating reasoning models and autonomous agents into business processesfrom accuracy trade-offs and productivity pitfalls to the psychological complexities of human-AI collaboration. We exp...
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 ...
