The Pragmatic Programmer for Machine Learning 1st Edition

The Pragmatic Programmer for Machine Learning — 1st Edition by Marco Scutari and Mauro Malvestio

Cut through academic theory and start building robust, production-ready machine learning systems today. This practical guide translates the principles of pragmatic software development into clear, actionable strategies for machine learning practitioners, data scientists, and engineers.

Packed with real-world examples and best practices, the book covers essential topics like feature engineering, model validation, reproducible workflows, deployment pipelines, and scalable architectures. It emphasizes code quality, testing, and maintainability so your models perform reliably in production — not just in notebooks. Written in a direct, user-friendly style, complex ideas are broken down into step-by-step guidance that you can apply immediately.

Whether you’re transitioning from research to industry or accelerating your team’s ML maturity, this 1st Edition equips you with workflows and patterns that reduce risk and speed delivery. Learn how to avoid common pitfalls, design resilient experiments, and integrate models into real systems while keeping auditing, monitoring, and iteration top of mind.

Ideal for professionals worldwide — from startups to enterprise teams across Europe, North America, Asia, and beyond — this book is a go-to reference for anyone who wants to make machine learning work reliably at scale. Practical, concise, and results-focused, it’s the manual modern ML practitioners need.

Order your copy of The Pragmatic Programmer for Machine Learning, 1st Edition and transform the way you build, test, and deploy machine learning solutions.

Note: eBooks do not include supplementary materials such as CDs, access codes, etc.