Applied Conformal Prediction: Reliable Uncertainty Quantification for Real-World Machine Learning in Python (Preorder / Early Access)
Applied Conformal Prediction: Practical Uncertainty Quantification for Real-World ML
Preorder / Early Access — chapters released progressively (updates included)
Most ML systems don’t fail because the model can’t predict — they fail because nobody knows when not to trust the prediction.
Applied Conformal Prediction is a practical, expert-level guide to turning point predictions into reliable prediction intervals and prediction sets — with coverage guarantees under clear assumptions — so you can make decisions with quantified risk rather than guesswork.
This is not a blog-style introduction. It’s a production-minded treatment of conformal methods for regression, classification, and time series, written for practitioners who deploy models and care about reliability, monitoring, and decision-making.
What you’ll learn
- How conformal prediction works (the real guarantees, not folklore)
- Prediction intervals for regression and prediction sets for classification
- Split conformal, cross-/jackknife-style variants, and practical tradeoffs
- Calibration and reliability: what holds under distribution shift and what breaks
- How to plug conformal methods into real pipelines (model-agnostic and model-specific patterns)
- How to use uncertainty outputs for thresholding, human-in-the-loop, and risk controls
Who this is for
- ML engineers and data scientists shipping models into production
- Researchers and practitioners who need valid uncertainty, not “probabilities that look nice”
- Teams working in higher-stakes settings where wrong predictions are costly
Early Access
This book is in active development. You get:
- Immediate access to released chapters
- All future updates as new chapters are published
- Early-access pricing before the final release
If you want your models to be not only accurate, but trustworthy, this book is for you.