The Theory and Practice of Enterprise AI - 2nd Edition by Ilya Katsov (Paperback)

Book Synopsis

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, large language models, 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.

Review Quotes

"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.

"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

"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, Activision

"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

"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

"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 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, Cars.com