inforcement 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.