Foundation Models Are Coming for Enterprise Databases [Jure Leskovec] - 768

In this episode, Jure Leskovec, co-founder and chief scientist at Kumo and professor of computer science at Stanford, joins us to explore two fronts of his work: AI for science and relational deep learning. We begin with AI Virtual Cell, a multiscale effort to learn data-driven representations from proteins to cells to patients using single-cell RNA-seq data, protein language models like ESM, and structure models like AlphaFold—without hand-encoding biology. Jure then dives into relational deep learning, reframing enterprise databases as graphs and training neural networks directly on raw multi-table data. He explains Kumo’s Relational Foundation Model (RFM2), which performs in-context learning over subgraphs to make accurate predictions on new databases and tasks with no training, and how this approach benchmarks against RelBench and other multi-table datasets. We also discuss real-world deployments at companies like Reddit, DoorDash, and Coinbase, explainability via attention over tables and columns, integration with agentic systems, deployment options, and practical limitations. 🗒️ For the full list of resources for this episode, visit the show notes page: https://twimlai.com/go/768. 🔔 Subscribe to our channel for more great content just like this: https://youtube.com/twimlai?sub_confirmation=1 🗣️ CONNECT WITH US! =============================== Subscribe to the TWIML AI Podcast: https://twimlai.com/podcast/twimlai/ Follow us on Twitter: https://twitter.com/twimlai Follow us on LinkedIn: https://www.linkedin.com/company/twimlai/ Join our Slack Community: https://twimlai.com/community/ Subscribe to our newsletter: https://twimlai.com/newsletter/ Want to get in touch? Send us a message: https://twimlai.com/contact/ 📖 CHAPTERS =============================== 00:00 - Introduction 01:53 - AI Virtual Cell project 07:39 - Computational social science and relational data 10:01 - Relational deep learning 18:31 - Use cases 20:44 - Limitations of single-table ML 23:52 - Multi-table relational benchmarks 27:25 - Kumo and RFM2 36:50 - Relational graph transformer and relational transformer papers 39:35 - Data requirements and in-context learning 42:53 - Benchmark results and real-world performance 44:59 - Cold start problems 45:51 - Kumo deployment use cases 49:38 - Explainability for relational foundation models 53:55 - Architectural bottlenecks and ideal use cases 59:02 - Spectrum on post-training and fine-tuning 01:01:25 - Future directions 🔗 LINKS & RESOURCES =============================== Kumo.AI - https://kumo.ai/ Relational Graph Transformer - https://arxiv.org/abs/2505.10960 Relational Deep Learning: Challenges, Foundations and Next-Generation Architectures - https://arxiv.org/abs/2506.16654 KumoRFM-2: Scaling Foundation Models for Relational Learning - https://arxiv.org/abs/2604.12596 Transformers for Tabular Data at Capital One with Bayan Bruss - 591 - https://twimlai.com/podcast/twimlai/transformers-for-tabular-data-at-capital-one 📸 Camera: https://amzn.to/3TQ3zsg 🎙️Microphone: https://amzn.to/3t5zXeV 🚦Lights: https://amzn.to/3TQlX49 🎛️ Audio Interface: https://amzn.to/3TVFAIq 🎚️ Stream Deck: https://amzn.to/3zzm7F5