Predictive modeling is a core element in modern systems, and powers capabilities such as fraud detection, loan approvals, and recommendation systems. These systems typically operate on structured, relational data stored in enterprise databases, with rows, columns, and interlinked tables. While computer vision and natural language processing have undergone a neural network revolution, the tabular data layer underpinning predictive modeling still largely relies on manual feature engineering and task-specific models.

Relational deep learning proposes a new approach. It treats databases as graphs and applies transformer-style attention mechanisms directly over structured relational data. Researchers are now building foundation models for tabular data that aim to generalize across predictive tasks without painstaking feature engineering.

Jure Leskovec is a Professor of Computer Science at Stanford University and he previously served as Chief Scientist at Pinterest and was an investigator at the Chan Zuckerberg Biohub. Most recently, he co-founded the machine learning startup, Kumo.AI.

In this episode, Jure joins Sean Falconer to discuss the limitations of traditional predictive modeling, why structured enterprise data requires its own modality-specific neural architectures, how graph transformers generalize attention to relational databases, and more.

Sean’s been an academic, startup founder, and Googler. He has published works covering a wide range of topics from AI to quantum computing. Currently, Sean is an AI Entrepreneur in Residence at Confluent where he works on AI strategy and thought leadership. You can connect with Sean on LinkedIn.

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