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Explore how Relational Foundation Models (RFMs) represent the next frontier in enterprise AI through this 49-minute podcast episode featuring Jure Leskovec, Professor at Stanford University and Chief Scientist at Kumo.AI. Discover why traditional foundation models that excel at text and images fall short in capturing the critical relationships that drive enterprise value, from customer-product connections to supplier-shipment networks. Learn how RFMs leverage advances in graph neural networks and large-scale ML systems to reason over interactions rather than isolated data points, enabling richer reasoning capabilities and delivering measurable business impact. Understand where relational modeling provides the biggest competitive advantages, how to construct the necessary data infrastructure to support these models, and strategies for operationalizing RFMs responsibly at enterprise scale. Gain insights into the evolution from traditional recommender systems to sophisticated relational models, the role of feature engineering in model performance, knowledge graph inference techniques, and the computational requirements for training these advanced systems. Examine practical applications in advertising model scaling, feature store evolution, and the integration of predictive AI with autonomous agents, while exploring how faster predictive models can enhance enterprise decision-making processes.
