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Foundation Models for Tabular Data
The Definitive Guide to In-Context Learning, PFN Theory, and Production Deployment for Structured Data
This book is 10% completeLast updated on 2026-03-26
Gradient-boosted trees have dominated tabular ML for a decade. A new class of pretrained models just broke through — making accurate predictions on unseen datasets in seconds, with zero gradient steps on your data. This book explains why it works, when it fails, and how to deploy it.
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About
About the Book
The first comprehensive reference on pretrained in-context learning models for structured data — from PFN theory to production deployment.
Gradient-boosted trees have dominated tabular ML for a decade. CatBoost, XGBoost, LightGBM. Every serious benchmark confirmed it.
That era is ending.
A new class of models — tabular foundation models — can make accurate predictions on previously unseen datasets without any dataset-specific training. No gradient descent on your data. No hyperparameter search. The training rows are the prompt.
This book is the definitive reference on the field. It covers the PFN framework and amortized Bayesian inference, seven architectural families (TabPFN-2.5, TabICLv2, Mitra, TabDPT, TabFlex, ConTextTab, TabSTAR), honest evaluation methodology that exposes benchmarking failures, deployment engineering including attention costs and KV caching, calibration, conformal prediction, and interpretability, and a practitioner decision framework for model selection.
Every claim is sourced. Every comparison is fair. Every architectural choice is explained from first principles.
If you're serious about production tabular ML, this is the book.
Author
About the Author
Valery Manokhin
Valery Manokhin (PhD, MBA, CQF) is a data scienstist, machine learning researcher and book author. He earned his PhD in ML at Royal Holloway, University of London, under Prof. Vladimir Vovk, the creator of Conformal Prediction, and holds an MBA from Warwick and an MSc in Computational Statistics & Machine Learning from UCL.
His technical books — Mastering Modern Time Series Forecasting and Applied Conformal Prediction among them — are used by data scientists, ML engineers, and researchers in over 100+ countries.
In parallel, he produces English editions of classic Russian mathematics textbooks, beginning with Kiselev’s Arithmetic and now Algebra, Part I. The aim is straightforward: give English-speaking students access to the books that shaped generations of Russian mathematicians, in editions that match the originals’ rigour.
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