Are Foundation Models Ready for Your Production Tabular Data?

Foundation models are revolutionizing fields like NLP and computer vision, but their application to tabular data faces challenges such as data heterogeneity and a lack of large, high-quality datasets. The content reviews specific foundation models for tabular data, starting with TabPFN, a Prior-Data Fitted Network that uses a transformer architecture and in-context learning for predictions on smaller tables. It then discusses CARTE, a model that transforms table rows into graph structures to capture context, processed by a graph-attentional network. The overall analysis questions whether these advanced models are ready for production use and how they compare to classic machine learning algorithms like Gradient Boosting.