Building and Training Generative AI Models : A Practical Guide to Generative AI Development and Scaling

This book is a hands-on, technical guide to building and deploying generative AI models using advanced deep learning architectures like transformers, GANs, VAEs, and diffusion models. Designed for AI engineers, data scientists, and ML practitioners, it offers a practical roadmap from data ingestion to real-world deployment and evaluation. The book starts by guiding readers on selecting the right model architecture for their application, be it text generation, image synthesis, or multimodal tasks. It then walks through essential components of model training, including dataset handling, self-supervised learning, and core optimisation techniques such as backpropagation, gradient descent, and learning rate scheduling. It also delves into large-scale training infrastructure, covering GPU/TPU usage, distributed computing frameworks, and system-level strategies for scaling performance. Practical guidance is provided on fine-tuning models with domain-specific data and applying reinforcement learning from human feedback (RLHF), model quantisation, and pruning to improve efficiency. Key challenges in generative AI—such as overfitting, bias, hallucination, and data efficiency—are addressed through proven techniques and emerging best practices. Readers will also gain insight into model interpretability and generalisation, ensuring robust and trustworthy outputs. The book demonstrates how to build scalable, production-ready generative systems across domains like media, healthcare, scientific simulation, and design through real-world examples and applied case studies.  By the end, readers will gain an understanding of how to architect, optimise, and apply generative models across diverse domains such as media creation, healthcare, design, scientific simulation, and beyond. What you will learn;Learn how to choose and implement generative models—VAEs, GANs, transformers, and diffusion models—for specific use cases. Master training optimization techniques such as backpropagation, gradient descent, adaptive learning rates, and regularization. Apply best practices for large-scale training using GPUs, TPUs, and distributed computing frameworks for performance scaling. Boost model efficiency through quantization, pruning, fine-tuning, and RLHF to enhance output quality and reduce overhead. Who this book is for:AI Engineers and Machine Learning Practitioners looking to build and deploy generative models in real-world applications.  Data Scientists working on deep learning projects involving text, vision, audio, or multimodal generation.