Machine Learning, End to End - From Foundations to Production: Martin Hander
English | September 20, 2026 | ISBN: 9798237456943 | 564 Pages | EPUB (True) | 44.28 MB
Most machine learning books stop where the real work begins. They show you how to train a model in a notebook, celebrate a good accuracy score, and leave you there, with no idea how to ship it, serve it under real traffic, catch the day its predictions quietly start to drift, or defend its decisions to a manager, a user, or a regulator. This book was written to close that gap.
Machine Learning, End to End is a single, coherent guide that treats machine learning as what it actually is in practice: an engineering discipline that begins with data and mathematics and ends with a reliable, monitored, responsibly-governed system. Across six carefully sequenced parts and thirty-two chapters, it carries you from first principles all the way to production without ever losing the thread.
You begin with the foundations, what machine learning can and cannot do, the essential math, and the craft of working with data and features. From there you move through the classical backbone of the field: regression, classification, trees and ensembles, honest model evaluation, and unsupervised learning. Part three steps up to deep learning, demystifying neural networks, convolutional and sequence models, generative and self-supervised methods, and the transformers and foundation models behind today's AI revolution, explained from the ground up so they stop being magic.
The book then equips you for the shapes real problems actually take: time series and forecasting, recommender systems, natural language processing, computer vision, causal inference and uplift modeling, probabilistic and Bayesian methods, and reinforcement learning. And then it does what almost no other single book does with this depth, it takes you into MLOps: data engineering, experiment tracking and reproducibility, deployment, monitoring and drift detection, cost and scalability, and the privacy, security, interpretability, and governance that make a system not just accurate but defensible.
It closes with three complete, end-to-end capstone projects, a tabular problem, an NLP or vision problem, and a full production pipeline, so you see every concept working together as one system rather than as isolated ideas. Six appendices provide lasting references: a math refresher, an evaluation-metrics cheat sheet, training tips, tools and libraries, curated datasets, and further reading.
Throughout, the emphasis is on judgment as much as technique. Every method is grounded in first principles, implemented in modern Python, and framed around the decisions that matter when latency, cost, fairness, and maintainability are on the line. Responsible AI is woven through the whole narrative rather than bolted on at the end.
If you can write basic Python and remember high-school algebra, you have what you need to begin. Whether you're a software engineer adding ML to your systems, a data scientist sharpening your production skills, a student pivoting into applied machine learning, or a technical leader who needs an honest map of the whole landscape, this is the book that takes you the entire way.
Stop building models that only work on your laptop. Learn machine learning the way it's actually practiced, and start building systems that survive contact with the real world.
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