Introduction

This book explores how optimised generative AI is driving industrial transformation. Written for real-world deployment, it shows how to move generative AI from the lab to the factory floor. It covers the scalability of large models such as GANs, LLMs and transformers, addressing computational efficiency, energy use, data privacy and ethical deployment. Chapters move from model architectures and optimisation techniques to applications in smart manufacturing, product design, industrial safety, engineering systems and decentralised finance, closing with self- optimizing systems and quantum computing.

Key Features :

Combines theory with real-world case studies from automotive, aerospace and healthcare. Offers frameworks for model compression for edge computing, quantisation, federated learning, human-AI collaboration, ethical AI governance and sustainable, energy-efficient AI.

Target Readership :

Researchers, data scientists, AI/ML engineers and industry practitioners in manufacturing, logistics and finance; graduate students, technology developers and decision-makers implementing AI.