You Don't Need to Train Models on Database Tables Anymore

Thanks to Neon for sponsoring this video. Check out Neon for your backend needs for your apps and agents: https://neon.plug.dev/zzzGgpR Google Research's TabFM is a zero-shot foundation model for tabular data: you pass it the rows you already know, and it predicts the rest in a single forward pass, with no training. In this video I explain how TabFM learns from synthetic tables, why it can't read column names, and how TabFM-Auto adds an LLM agent around it. Then I build that agent on Neon Postgres to predict customer churn, and show how a leaky column fooled the agent and how a branch of the database's history fixed it. Neon: https://neon.plug.dev/zzzGgpR TabFM blog post: https://research.google/blog/introducing-tabfm-a-zero-shot-foundation-model-for-tabular-data/ TabFM paper: https://arxiv.org/abs/2609.37959 TabFM-Auto paper: https://arxiv.org/abs/2609.37989 TabFM on GitHub: https://github.com/google-research/tabfm TabFM on Hugging Face: https://huggingface.co/google/tabfm-1.1.0-pytorch