8 New AI Models Books Shaping the Industry in 2025

Omar Sanseviero and his co-authors leverage their deep industry experience to demystify generative AI techniques, focusing on transformers and diffusion models. You’ll gain hands-on skills to build and customize models that generate text, images, audio, and music, using real code examples and open source libraries like transformers and diffusers. The book walks you through fine-tuning pretrained models and even training your own, with practical insights on trade-offs and creative applications across domains. If you’re involved in AI development or research and want to move beyond theory to actual implementation, this book offers concrete, up-to-date guidance rooted in recent advances.

What happens when deep expertise in AI engineering meets the emerging challenges of edge computing? This book by Pethuru Raj Chelliah and co-authors dives into federated learning, a technique gaining traction for optimizing AI models across decentralized environments. You’ll explore how AI models can be refined, compressed, and deployed efficiently on public, private, hybrid, and edge clouds, learning about frameworks like PySyft and TensorFlow Federated. Chapters cover practical AI applications such as computer vision and NLP, while also addressing critical concerns like data privacy and cybersecurity in distributed systems. If you’re involved in AI development or infrastructure aiming to leverage federated learning for real-world efficiency, this book offers a detailed technical foundation without unnecessary fluff.

This tailored book explores the latest breakthroughs and developments transforming AI models in 2025, crafted to align with your current knowledge and future goals. It covers emerging research, innovative architectures, and new approaches shaping AI’s evolution, focusing on the aspects most relevant to your background. By examining recent discoveries and advances, it offers a personalized deep dive into how AI models are adapting, improving, and expanding across diverse applications. Readers engage with cutting-edge content tailored to interests in generative models, optimization, and deployment techniques, making complex innovations accessible and practical. This personalized approach ensures you focus on the new frontiers that matter most to your growth and curiosity.

During her work in software development, Banu Parasuraman noticed the growing need for Java developers to effectively harness large language models and generative AI within familiar frameworks. This book guides you through integrating these advanced AI technologies using the Spring Framework, starting with foundational AI concepts and progressing to advanced application development. You’ll learn not only how to build AI-driven Java applications but also how to address critical topics like security and ethics in AI deployment. If you’re an intermediate to advanced Java developer looking to bridge your existing skills with cutting-edge AI, this book provides a focused and practical roadmap.

After analyzing how large language models (LLMs) are reshaping software development, John Berryman and Albert Ziegler crafted this guide to the art and science of prompt engineering. You learn not just the technical architecture of LLMs but also how to design effective prompts that enable these models to perform complex tasks reliably. The book covers specific techniques like few-shot learning, chain-of-thought prompting, and retrieval-augmented generation, giving you tools to build smarter AI applications. If you're developing AI-driven software or curious about enhancing LLM interaction, this book offers a clear framework to elevate your skills.

What happens when a finance professor with over two decades of coding experience tackles generative AI? Mark Liu delivers a hands-on guide that demystifies building generative AI models using PyTorch, a popular Python framework. You’ll explore creating diverse projects like English-to-French translators, text generators rivaling GPT-2, and image diffusion models, all while mastering core concepts such as GANs, Transformers, and Large Language Models. This book suits Python programmers familiar with basic machine learning who want to deepen their practical skills in generative AI without prior PyTorch experience.

This tailored book explores the emerging landscape of AI models expected to dominate in 2025 and beyond, focusing on your unique role and objectives. It covers the latest discoveries and advancements, personalized to match your background and interests, and delves into how these innovations could impact your work. With a focus on staying ahead, it examines cutting-edge techniques, emerging research, and future trends in AI modeling, providing a focused learning journey that aligns with your specific goals. By tailoring content to your preferences, this book offers a streamlined exploration of tomorrow's AI models without extraneous information, enabling you to engage deeply with the most relevant developments.

Drawing from their deep expertise in AI and data analytics, HARI C KRISHNA and Nitin Talreja offer a practical guide that demystifies artificial intelligence by focusing on both foundational theories and hands-on applications using KNIME. You’ll gain concrete skills in building AI models, including machine learning and natural language processing, supported by real case studies and exercises that clarify complex concepts. The book also tackles ethical issues and future trends, making it relevant whether you’re a data scientist, business leader, or tech enthusiast aiming to understand or implement AI effectively. This isn’t just theory; it’s a toolkit for navigating AI’s evolving landscape with clear examples and quizzes to reinforce your learning.

Drawing from the latest developments in AI, Liam Bennett crafts a focused guide that helps you apply AI models directly to revenue operations challenges. You’ll learn how to build predictive sales forecasts, segment customers with precision, and implement dynamic pricing strategies that respond to market demands. The chapters are packed with practical frameworks and exercises, such as using automation to streamline workflows and detect churn risks, making the concepts tangible. This book suits RevOps leaders and data-driven strategists eager to harness AI’s potential for measurable business growth, rather than theoretical AI enthusiasts.

Drawing from their deep expertise in AI and natural language processing, TransformaTech Institute crafted this guide to demystify large language models like GPT-4o and BERT. You’ll gain a solid grounding in machine learning and NLP fundamentals before tackling the complexities of transformer architectures, including self-attention and encoder-decoder frameworks. The book doesn’t just explain theory—it walks you through building, training, and fine-tuning models, illustrated with practical case studies on chatbots and content generation. If you want to understand how these AI models work and how to apply them, this book delivers clear, focused instruction without fluff or jargon.