Mastering Generative AI Systems Engineering

Create, Imagine, and Innovate with the Power of Generative AI

Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com
● Comprehensive coverage of generative models—from VAEs and GANs to Diffusion and LLMs.
● Hands-on projects using PyTorch, TensorFlow, LangChain, and modern AI toolchains.
● Clear mathematical explanations that connect theory with practical model building.
● Real-world case studies across computer vision, NLP, data augmentation, and AI deployment.

Book Description
Generative AI is rapidly transforming how organizations create content, build intelligent systems, and automate complex tasks. Understanding how these models work—and how to build them—is now a career-defining skill for developers and data professionals.

Mastering Generative AI Systems Engineering begins with the core foundations of generative AI. You will explore the essential mathematics, latent spaces, probability concepts, and neural network principles behind VAEs and GANs.

The book then guides you through advanced systems such as CycleGANs, StyleGANs, and cutting-edge Diffusion Models—the engines behind today’s most powerful generative tools. The journey continues with LLMs and GPT-based systems, covering prompt engineering, RAG pipelines, LangChain applications, and agentic AI workflows. Thus, by the end, you will be ready to design and build powerful generative AI systems—from image generators and translation tools to intelligent assistants and custom LLM-powered applications.

What you will learn
● Design, train, and fine-tune state-of-the-art GANs, VAEs, and diffusion models.
● Build powerful LLM and GPT-based applications using RAG, LangChain, and agentic workflows.
● Apply core mathematical concepts to understand and optimize generative architectures.
● Develop real-world AI solutions for image synthesis, NLP, and multimodal tasks.
● Evaluate, optimize, and deploy generative models for scalable production systems.
● Implement ethical, responsible, and safety-driven practices for generative AI development.

Who is This Book For?
This book is designed for machine learning engineers, data scientists, AI developers, NLP engineers, computer vision specialists, research scientists, and software engineers aiming to advance their expertise in generative AI. Readers should have a basic knowledge of Python, deep learning fundamentals, and familiarity with neural networks to fully benefit from the hands-on projects and real-world case studies.

1. Introduction to Generative Models
2. Mathematical Foundations
3. Introduction to Variational Autoencoders
4. Introduction to Generative Adversarial Networks
5. Deep Convolutional GANs
6. Conditional Generative Adversarial Networks
7. Cycle GANs
8. Style GANs
9. Variational Autoencoders Revisited: β-VAE and CVAE
10. Diffusion Models
11. Data Augmentation with Generative Models
12. Generative Models in Natural Language Processing
13. Model Evaluation and Optimization
14. Deployment of Generative Models
15. Ethical Considerations and Future Directions
16. Introduction to Large Language Models
17. Generative Pre-Trained Transformers
18. Langchain: Building AI-Powered Applications
19. Prompt Engineering, RAG, and Fine-Tuning
20. Advanced Concepts
21. Best Practices for Generative Models
Index

Praveen Kumar is a global tech leader with more than 27 years in IT, having held CTO, VP, and architect roles across the USA, Canada, China, and Europe. An expert in AI, ML, Generative AI, Big Data, and cloud technologies, he has received state and national awards in software development and frequently delivers technology talks while mentoring developers worldwide.

About the Technical Reviewer
Rahul Vats is a distinguished Data and AI engineering leader with over 15 years of experience in architecting and delivering innovative, AI-driven solutions for Fortune 500 clients. He specializes in Generative AI, Microsoft Copilot development, Retrieval-Augmented Generation (RAG) architecture, and Agentic AI systems, enabling enterprises to implement scalable, intelligent, and cost-efficient solutions.

Currently serving as a Senior Lead (Manager) at Capital One, Rahul provides technical leadership in establishing robust guardrails and standards to remediate and control non-compliance across diverse cloud environments, including AWS, Azure, and GCP. Throughout his career, his technical and strategic initiatives have driven impactful advancements in Microsoft Copilot integrations, significantly enhancing productivity through intuitive, AI-driven experiences. Rahul's expertise in RAG architecture has been pivotal in building scalable knowledge retrieval pipelines that empower enterprise search, customer support automation, and decision-support systems. By implementing modular and adaptive frameworks, he has enhanced system scalability, improved contextual relevance, and streamlined information retrieval for enterprise users.

Rahul’s deep technical proficiency, coupled with his leadership in AI engineering, has established him as a trusted partner in driving innovation, empowering teams, and delivering impactful