The Theory and Practice of
Enterprise AI 2nd edition

by Ilya Katsov

Advancements in deep learning, reinforcement learning, and generative AI have dramatically extended the toolkit of machine learning methods available to enterprise practitioners. This book provides a comprehensive guide to how marketing, supply chain, and production operations can be improved using these new methods, as well as their use in conjunction with traditional analytics and optimization approaches. The book is written for enterprise data scientists and analytics managers, and will also be useful for graduate students in operations research and applied statistics.

The Theory and Practice of Enterprise AI is divided into five parts. Part I introduces the basic concepts of enterprise decision automation, deep learning, generative AI, and reinforcement learning methods. Part II presents recipes for customer analytics and personalization. Part III describes search, recommendations, knowledge management, and media generation solutions that are focused on content data such as texts and images. Part IV discusses methods for demand forecasting, price optimization, and inventory management. Finally, Part V presents blueprints for anomaly detection and visual inspection that help to improve production and transportation operations. Python code examples are provided in the complementary online repository to support the reader's understanding of the implementation details.

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Print length

650 pages



Publication date

June 1, 2023 




Grid Dynamics

Trim size

7 x 10 inches

Editorial Reviews

"A must read primer for any data science leader. Ilya has taken on the Herculean task of systemizing AI-based problem solving in a business setting, and has succeeded spectacularly. This book is of interest to all kinds of analytics practitioners as it comes with real-world examples for the curious, and an abundance of theoretical explanations for the audacious."

Suman Giri, Head of Data Science,
Merck & Co.

"Ilya Katsov's previous book set the standard as the clearest, most complete, and self-contained treatment of modern algorithmic marketing that I'm aware of - I have used and recommended it many times. Now he applies the same level of expert guidance providing a one-stop-shop for deep/reinforcement learning techniques in marketing, supply chain, and operations. This book will sit within arm's reach for years to come."

Spencer Stirling, Director of Data Science,

"This is a unique book in that it dives into the depths of machine learning theory while still being organized around business applications and use-cases. By presenting a detailed understanding of machine learning algorithms alongside their applications, this text is versatile -- applicable to a variety of users from technical students to data scientists and all the way to data and IT leadership. A very valuable addition to our Data Science world."

Ellie Magnant, Director of Data Science,
UnitedHealth Group

"The book is a resource where algorithmic theory, algorithmic system design, and their applications are strongly tied to each other and discussed in depth. It offers a guide to technical leaders on how to make their systems more actionable, technically sound, and applicable at various scales. To business leaders, this book helps connect the dots and offers ideas on how to improve their current processes to have a meaningful communication with AI practitioners."

Addhyan Pandey, Senior Director of Data Science,

"The Theory and Practice of Enterprise AI combines cutting-edge AI modeling concepts, academic rigor, and actionable industry domain knowledge in one concise tome. It is an absolute must on any data science practitioner's book shelf or desk."

Skander Hannachi, AI/ML Specialist,
Google Cloud

"The Theory and Practice of Enterprise AI builds on the excellent foundation of its predecessor Introduction to Algorithmic Marketing. It offers a practical and rigorous guide for applying AI to achieve business goals. Ilya Katsov covers the essential aspects of designing and deploying AI systems for the enterprise. The book is written in a clear, concise, and engaging way that appeals to technical, business practitioners, and non-technical audiences alike. It also provides numerous business examples and use-cases that are supported by a rich online library of code snippets in Github across various domains such as marketing, supply chain, and manufacturing. As a business analytics manager with hands-on experience, I find Enterprise AI very useful for framing my business problems and taking the next steps to improve business outcomes significantly. This book is a great investment of your time if you want to deploy AI models successfully."

Juan B. Solana, Global Director Measurement and Advanced Analytics,
General Motors

"This book provides technical depth and reference architecture to solve real-world business problems using AI. The book will resonate with the data science community, and this book is an excellent addition to AI literature. Ilya has done a fantastic job bringing theoretical and practical AI closer with this book."

Prateek Srivastava, Director of AI Products,
Dell Technologies

"This book is an excellent introduction to machine learning and its applications in enterprise. It is a great resource for data scientists looking for bridging theory and practice - it presents many distinctly different business use cases and clearly shows how state of the art methods in AI can be applied, with complete reference implementations provided in interactive notebooks. In a world where AI is increasingly present in all parts of businesses this is a comprehensive guide with everything you need to know."

Anna Ukhanova, Research Technical Program Manager,
Google AI

"Excellent. I strongly recommend this book for anyone involved in Enterprise AI for a great overview of solutions for key marketing, supply chain & production business processes."

Joost Bloom, Head of Machine Learning & Foundational AI,
H&M Group

"The biggest challenge for the adoption of enterprise AI is the divide that separates theory from application. Ilya bridges it elegantly. The book covers all important AI techniques of the recent years in great detail. Importantly, the reader is not left alone with the theoretical knowledge. Ilya discusses real-world business problems and presents state-of-the-art solutions utilizing the concepts taught in this book. A must-read for anyone eager to keep up with the rapidly evolving field of enterprise AI."

Jan Scholz, Senior Director of Data Science,
Loblaw Companies Limited

"This textbook provides an ultimate guide to data scientists and AI engineers on building best-in-class AI capabilities to solve a wide spectrum of business problems. Furthermore, Ilya did a great job covering the end-to-end development lifecycle of AI solutions with practical case studies. Excellent book, Highly Recommended."

Fouad Bousetouane, Senior Principal Machine Data Scientist,
W.W. Grainger, Inc.

"The book by Ilya Katsov equips technical and data/AI professionals working in the Marketing, Supply Chain and Production Operations domains with modern techniques and approaches for solving a broad range of AI problems. The book provides a very systematic overview of methodologies and does a fantastic job in explaining rationale and assumptions for their use. This makes this book suitable not only for entry level but also for expert AI professionals. Finally the book provides multiple case studies which make it even more valuable for practitioners."

Alexander Statnikov, Head of Go-to-Market Platform and Ecosystem Products,

"Many books on AI and ML aim to bridge the chasm between theory and practice, but Ilya's is one of those rare few that succeeds brilliantly in doing just that. If you are an ML practitioner looking for a very application-oriented book on enterprise AI, this book should be on your must-read list. Because of the well-explained rationale and math behind the models, it will serve as a great reference book for not only the data science community but also for seasoned marketing and supply professionals."

Tushar Kumar, Global Head of Analytics,
Signify (f/k/a Philips Lighting)

"Timely and inspiring! An essential handbook for those seeking a primer on the data science community, from enthusiastic to novice to developers to resident experts interested in scalable enterprise AI and ML for any industry domain. Ilya paints a vision of what's possible and aligns it with the business problem to guide the journey toward solution options & architecture, model architecture, implementation plan, reference code, and modeling prototype. In addition, Ilya has created a very comprehensive framework for organizing and prioritizing the key building blocks of model representation and mapping, customer experience, content intelligence, revenue & inventory management, and production operations. With Ilya's breadth of experience in the field of enterprise AI, this book should be considered the de-facto reference guide for any organization undergoing a digital AI transformation. If you can't work directly with Ilya, this is a close second best! Enjoy, Learn & Apply... and watch what happens..."

Srikanth Victory, Vice President, Digital Advanced Analytics & Products,
CommonSpirit Health


Find models from this book on GitHub at There is a dedicated branch "book-enterprise-ai-edition-2.1" that contains a version of code compatible with this book.

About the Author

Ilya Katsov is a VP of Technology at Grid Dynamics, a global consulting company specializing in emerging technology innovations for large enterprises. Ilya leads the development of data science and AI solutions that help companies improve customer experiences, internal processes, and physical operations. Prior to joining Grid Dynamics, Ilya worked at Intel Research on wireless communication technologies. He is the author of two books: