Machine Learning is critical to success in many enterprises today. From running predictive analytics to understand customers buying intentions in order to sell more products to reducing cost within a supply chain, each machine learning model is performing a critical business function. These are so critical in fact that the people behind these operations (that’s you!) must become masters of data, algorithm ninjas, and problem solving geniuses. With the variety of specific skills and business objectives it’s no wonder our list of the 9 best machine learning books covers a myriad of topics, disciplines and focus areas.
1. Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps
By Valliappa Lakshmanan, Sara Robinson and Michael Munn.
Recommended by Tanu Chellam, VP Product Seldon. I read this before I joined Seldon to brush up on some of my knowledge gaps, and even now I still refer back to it when I need to. It’s been a valuable resource for me.
Written by three Google engineers, this book uses 30 in-depth examples to explain how to solve common challenges when designing, training, evaluating and deploying ML models.
By Christoph Molnar
This book provides a nice holistic overview of the ML explainability landscape, covering various aspects such as inherently interpretable models, and various post-hoc instance level explanations for both white-box or black-box settings or global explanations. They explain everything with nicely worked examples which are easy to understand, without oversimplifying the technical aspect of the algorithms.
By Emmanuel Raj
One of the top machine learning books available, this is a great overview of the full MLOps lifecycle from workflows right through to programming, training and deploying models.
4. Accelerate: The Science of Lean Software and Devops: Building and Scaling High Performing Technology Organizations
By Nicole Forsgren, Jez Humble, Gene Kim
Ideal for business and IT leaders, Accelerate uses real world examples of streamlined software delivery processes, highlighting the various successes and failures of each. By reading this book you’ll know how to run DevOPs successfully in your organisation – and avoid the pitfalls encountered by other businesses.
By Jacob Turner
Not a technical book by any means but worthy of being on the list. Who is liable if an algorithm causes harm? Can an AI or robot have legal protection and rights? How do we know what’s right and wrong from an ethical decision making view point? These are some of the complications facing the world of AI and this book will open your eyes to the considerations of the future of technology.
By Oliver Theobald
No list of books would be complete with a novice option, and this is our favourite. When it comes to machine learning and data science things can go deep very quickly, this doesn’t. Perfect for those who work in tech but aren’t necessarily on the engineering side.
By Azeem Azhar
For many years Akhar has written about the increasing gap between the pace of technological change and the ability of institutions to keep up. In Exponential he has created a practical playbook that combines economics, political science and psychology with the power of technology to close the gap. In doing so, Azhar argues that technology can be turned to solving our ‘real’ needs in terms of work, politics and even national defence.
By Kai-Fu Lee
AI is set to change the world, but it is not always to see its impact on the global scale. Drawing on his experience of working in both countries, Lee explains how the USA and China are fighting to assert their dominance. He explains how AI will significantly affect the workforce and proposes some practical solutions to the most profound changes that will affect us all.
By Ed Shee
Ok not actually one of the machine learning books, but it’s still a bonus. Join us for our Shape the Future sessions hosted by Ed Shee, Head of Developer Relations at Seldon, as we discuss everything Machine Learning Operations with MLOps professionals and Seldon thought leaders.