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 ...
E163 - Reliable AI systems at scale - Göran Sandahl
AIAW Podcast
2 hours 9 minutes
2 months ago
E163 - Reliable AI systems at scale - Göran Sandahl
In Episode 162 of the AIAW Podcast, we’re joined by Göran Sandahl, Co-Founder of Opper AI, to explore the future of building reliable AI systems at scale. Göran shares his journey from observability engineering to co-founding Opper AI, a company tackling one of the most urgent challenges in enterprise AI: making LLM-based features predictable, testable, and production-ready. We dive into Opper’s structured API approach, the strategic vision behind their recent acquisition of FinetuneDB, and h...
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 ...
