AI Reading List: Suggestions for the book club
AI Reading List
The book club I’m a part of is finishing up AI Engineering and we’re on to the next book, which is TBD at this point.
Here are some books I’m putting forward as potential candidates for the next book:
The Art of AI Product Development
by Dr. Janna Lipenkova
The Art of AI Product Development offers a clear, practical approach to creating products that use AI. It provides real-world guidance on defining your AI strategy, developing useful AI features, and supporting user trust and adoption. Rather than chasing trends, the book focuses on core principles and long-term thinking—foundations that remain relevant as the field evolves.
Agentic AI
by Matt Carlson
Agentic AI isn’t another vague “AI is the future” book. It’s a hands-on, field-tested roadmap for integrating Agentic AI into your business with clarity, confidence, and measurable ROI.
AI Product Manager’s Handbook
by Irene Bratsis
This book provides a detailed roadmap for successfully building and maintaining AI-driven products, serving as an indispensable companion on your journey to becoming an effective AI product manager. In this second edition, you’ll find fresh insights into generative AI, and responsible AI practices with the most relevant tools for building AI-powered products.
Building Business-Ready Generative AI Systems: Build Human-Centered Generative AI Systems with Agents, Memory, and LLMs for Enterprise
by Denis Rothman
In today’s rapidly evolving AI landscape, standalone LLMs no longer deliver sufficient business value on their own. This guide moves beyond basic chatbots, showing you how to build advanced, agentic ChatGPT-grade systems capable of sophisticated semantic and sentiment analysis, powered by context-aware AI controllers.
The Rise of AI Agents: Transforming Professional Landscapes: Pioneering Innovations that Redefine Work and Learning Environments
by Adrian Blake
In The Rise of AI Agents: Transforming Professional Landscapes by Adrian Blake, step into the evolving world where artificial intelligence is not just an aid but a cornerstone in reshaping industries. This insightful exploration showcases how AI agents are automating tasks and innovating solutions across various sectors including healthcare, education, and beyond. Understand the profound impact of AI personal assistants and virtual tutors as they enhance user experience and streamline workflows, fostering an environment where professionals are empowered to advance their careers like never before.
LLM Design Patterns: A Practical Guide to Building Robust and Efficient AI Systems
by Ken Huang
This practical guide for AI professionals enables you to build on the power of design patterns to develop robust, scalable, and efficient large language models (LLMs). Written by a global AI expert and popular author driving standards and innovation in Generative AI, security, and strategy, this book covers the end-to-end lifecycle of LLM development and introduces reusable architectural and engineering solutions to common challenges in data handling, model training, evaluation, and deployment.
Building Agentic AI Systems: Create intelligent, autonomous AI agents that can reason, plan, and adapt
by Anjanava Biswas, Wrick Talukdar
Gain unparalleled insights into the future of AI autonomy with this comprehensive guide to designing and deploying autonomous AI agents that leverage generative AI (GenAI) to plan, reason, and act. Written by industry-leading AI architects and recognized experts shaping global AI standards and building real-world enterprise AI solutions, Building AI Agentic Systems explores the fundamentals of agentic systems, detailing how AI agents operate independently, make decisions, and leverage tools to accomplish complex tasks.
Hands-on Machine Learning with Scikit-learn, Keras and TensorFlow
by Aurélien Géron
By using concrete examples, minimal theory, and two production-ready Python frameworks, Scikit-Learn and TensorFlow author Aurélien Géron helps you gain an intuitive understanding of the concepts and tools for building intelligent systems. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. With exercises in each chapter to help you apply what youâ??ve learned, all you need is programming experience to get started.
The Elements of Statistical Learning
by Trevor Hastie, Robert Tibshirani, Jerome Friedman
This book describes the important ideas in a variety of fields such as medicine, biology, finance, and marketing in a common conceptual framework. While the approach is statistical, the emphasis is on concepts rather than mathematics. Many examples are given, with a liberal use of colour graphics. It is a valuable resource for statisticians and anyone interested in data mining in science or industry. The book’s coverage is broad, from supervised learning (prediction) to unsupervised learning. The many topics include neural networks, support vector machines, classification trees and boosting---the first comprehensive treatment of this topic in any book.