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 ...
E156 - Transforming Enterprise AI in the Data Cloud - Mats Stellwall
AIAW Podcast
1 hour 51 minutes
7 months ago
E156 - Transforming Enterprise AI in the Data Cloud - Mats Stellwall
Join us for Episode #156 of the AIAW Podcast, where Snowflake EMEA’s Principal Architect AI/ML and Field CTO, Mats Stellwall, walks us from his SPSS beginnings in the 1990s to today’s cutting‑edge Generative AI, revealing why Snowflake’s Data Cloud is the premier platform for scaling AI/ML workloads. We’ll unpack game‑changing tools like Agentic AI, Cortex Analyst, and Cortex Search, dive into real‑world enterprise use cases, navigate regulatory and ethical challenges, and peer into the futur...
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 ...
