Generalize: Building Models That Don’t Break

A model that works once is not enough. Generalize is a machine learning book devoted to building systems that perform reliably beyond training data, benchmarks, and controlled environments.
The writing addresses one of ML’s hardest problems: generalization. Readers explore why models fail in production, how overfitting hides behind strong metrics, and why real-world data rarely behaves as expected. Robustness, not optimization alone, becomes the central goal.
Rather than offering shortcuts, the book emphasizes discipline. It covers data diversity, validation strategy, stress testing, and monitoring—showing how resilience is engineered intentionally. Each chapter connects modeling decisions to long-term stability under change.
The tone is rigorous and grounded, aimed at practitioners who value durability over demos. Language remains clear and methodical, reinforcing habits that prevent brittle systems.
Generalize moves through data splits, bias control, regularization, evaluation under shift, and post-deployment monitoring—demonstrating how models earn trust by surviving reality.
Key themes explored include:
• Generalization beyond training data
• Overfitting and robustness
• Evaluation under distribution shift
• Reliability in production
• Building resilient models
Generalize is for builders who want longevity—offering guidance to create models that keep working when conditions change.