Google Research· Research

Introducing TabFM: A zero-shot foundation model for tabular data

Weihao Kong and Abhimanyu Das, Research Scientists, Google Research We’ve seen a massive shift in how people handle time-series forecasting since we launched TimesFM. Now, we’re bringing that same "zero-shot" logic to tabular data. We introduce TabFM, a new foundation model for tabular data to simplify classification and regression workflows. Quick links Hugging Face GitHub Share Copy link × Tabular data constitutes the backbone of enterprise data infrastructure and powers a significant fraction of critical predictive machine learning applications . From predicting customer churn to identifying financial fraud, tabular regression and classification tasks are ubiquitous. For years, supervised tree-based algorithms like AdaBoost , XGBoost and random forests , to name a few, have historically dominated this space, offering robust performance on structured data. However, the lifecycle of deploying these traditional models presents a significant bottleneck. Fitting an XGBoost model to a new dataset is not merely a matter of a single .fit() step; it invariably requires tedious manual effort. …

Announced

30 Jun 2026 10:26 UTC