The Sequence AI of the Week #891: Prompting a Spreadsheet: Inside Google's TabFM for Tabular AI
Addressing one of the biggest use cases in enterprise AI.
TL;DR
- The Sequence is reader-supported and has operated without sponsors for over two years.
- Enterprise machine learning often relies on gradient-boosted trees for tasks like churn and fraud prediction.
- The traditional workflow for tabular data involves feature engineering, cross-validation, and hyperparameter tuning.
- Google Research's TabFM is a foundation model for tabular classification and regression.
- TabFM makes predictions on unseen tables in a single forward pass, without requiring training, tuning, or feature engineering.
- The model operates using in-context learning, where the entire problem is given as a prompt.
- TabFM follows the approach of Google's previous model, TimesFM, which was a foundation model for time-series data.