Machine Learning Engineering

This book is an exceptional follow through on the part of the author of the 100 page machine learning book. He covers the 'engineering' of machine learning from start to finish. The 100 page machine learning book introduces the reader to machine learning algorithms and the 'math' behind the magic. However, deploying a machine learning solution is much more than the model. The author clearly outlines the principles once must understand to successfully deploy a machine learning solution. I particularly enjoyed Sections 1.4 and 1.5 when to use and when not to use machine learning. From the discussion one can clearly set forth the criteria establishing when one should pursue a machine learning solution and when one should pursue other alternatives. A brief stop in each section will undoubtedly save many both valuable time and frustration. Overall, an excellent work. If you are interested in machine learning I highly recommend this book as well as 'The 100 Page Machine Learning Book.'

It is an excellent read for anyone looking to leverage ML to solve business problems at scale. Andriy has done a great job in breaking down tasks needed to move a model to production. It is a perfect follow up to his first book- Hundred Page ML which is a great read as well.

If you need a weeks worth of ML reading, read this. If you want to be(come) a practitioner, this will be a very basic primer.

Machine Learning Engineering by Andriy Burkov is a must-have for anyone serious about building real-world ML systems. While many books teach you how to train a model, Burkov shows you how to turn that model into a reliable, scalable, and maintainable product — exactly what every company needs today. The book is packed with practical insights on every stage of the ML lifecycle, from scoping and data management to deployment, monitoring, and maintenance. Burkov’s writing is clear, concise, and actionable. He cuts through the noise and focuses on what actually matters when engineering ML solutions in production environments. What I love most is how Burkov emphasizes best practices from leading tech companies, yet explains them in a way that’s accessible even if you’re not working at a tech giant. It’s a perfect blend of theory, practical advice, and real-world experience. If you’re a machine learning engineer, data scientist, software developer, or even a technical manager, this book will level up your understanding and help you avoid the many pitfalls of ML deployment. 5/5 stars — Every serious ML practitioner should read this book!

For an aspiring data scientist wanting to learn how to develop models, there has been no shortage of resources. But learning how to manage the ML lifecycle and put the models into production has been the real challenge. Andriy Burkov follows up his previous great ”The 100 page Machine Learning Book” with this excellent new book. Highly recommended.

I graduated 11 months ago and have been working as a Data Scientist since. Finishing this book makes me feel like I've worked on orders of magnitude more projects than I have. The examples are very clear and you're always left feeling like you understand WHY each topic is important. Not only did this book teach me new things about MLE but it helped cement things I already figured out for myself on the job. More often than not, Andriy's words are an invaluable reinforcement for concepts I thought I already knew. Highly recommended.

This is a must read for any data scientist looking to transition to a ML engineer role. I have over 3 years of experience in data science at a leading financial services firm and I must say this book has taught me so many new things. This is a treasure of gold written by Andriy. I have read the 100 pages ML book too. Both of his books are a must read and can be a good daily reference.

Like the first one this is an encyclopedia for machine learning. This is not really for beginners or practitioners. All the topics here could be found on the internet. What the book does well if compile a bunch of topic on machine learning together that someone could use for more research. In fact, the authors seems to encourage that throughout. What the book doesn’t do well is explain machine learning. The examples are disconnected. The author jumps in and out of formulas without introducing them or connecting them to text. I think the book would benefit from a chart that pulls it all together.