Mastering AI System Design

From Whiteboards to Workloads - Bridging AI Theory and Practice.

Key Features

● Practical frameworks, trade-off discussions, and mock interviews to prepare for modern system design.
● Master LLMs, RAG, fine-tuning, edge AI, and multimodal systems through practical, domain-specific examples.
● Connects academic AI foundations with industrial implementations to help readers design end-to-end systems.

Book Description

System design is now a critical skill for AI professionals, enabling them to integrate data pipelines, model serving, orchestration, and monitoring into cohesive production ecosystems. Mastering AI System Design will guide you through that complete journey—from understanding design principles and data workflows to building deployable AI architectures. It introduces core components of AI system design such as data engineering, model selection, evaluation metrics, API integration, and lifecycle management.

Each chapter blends theory, architecture diagrams, and code-driven blueprints that cover real-world use cases—LLMs and prompt engineering, Retrieval-Augmented Generation (RAG), fine-tuning, supervised and unsupervised learning systems, recommendation engines, edge AI deployment, and multimodal transformers.

By the end, you will be well-equipped to analyze trade-offs, design scalable inference pipelines, ensure model reliability, and apply system design frameworks for interviews and enterprise AI applications with confidence.

What you will learn

● Build end-to-end AI systems using proven frameworks for both interviews and real-world projects.
● Design and implement LLM architectures, RAG pipelines, and fine-tuned models with hands-on guidance.
● Develop supervised, unsupervised, recommendations, and multimodal AI systems across industries.
● Architect domain-specific LLMs, sequence-to-sequence models, and edge-optimized vision systems.
● Optimize, evaluate, and monitor AI systems for scalability, reliability, and performance.
● Leverage modern AI tools and libraries including LangChain, Hugging Face, PyTorch, and TensorFlow.

Who is this book for?

This book is designed for AI Engineers, Data Scientists, Machine Learning Engineers, MLOps Practitioners, Applied Researchers, and Technical Architects aiming to master AI system design. It is equally valuable for graduate students, educators, and interview aspirants preparing for roles in applied AI and data science.

Readers should have a working knowledge of Python, machine learning fundamentals, and basic model development to fully benefit from the hands-on case studies and system blueprints presented in this book.

1. Introduction to AI System Design
2. Crafting Intelligent Systems Using Prompt Engineering
3. Developing Retrieval-Augmented Generation Systems
4. Enhancing Systems Through LLM Finetuning
5. Designing Financial Risk Prediction Systems Using Supervised Learning
6. Implementing Unsupervised Learning Systems
7. Building Recommendation Systems for E-Commerce
8. Building Image Classification Models for Edge Devices
9. Designing Sequence-to-Sequence Systems
10. Building Domain-Specific LLMs from Scratch
11. Building Multimodal Applications for Healthcare
Index

Soudamini Sreepada is a distinguished leader in Artificial Intelligence, education, and research, with over 18 years of experience in the technology industry. She teaches AI system design through DesignYourAI - https://www.designyourai. in. Her projects and resources are available on GitHub at https://github.com/ soudaminigit/

After earning her M.Tech. in Computer Science from IIT Bombay in 2003, she built a remarkable career at Microsoft India Pvt. Ltd., where she held roles such as Software Engineer, Manager, and Principal Data Scientist. At Microsoft, she contributed to flagship products including Windows and Bing, spearheading initiatives that combined large-scale engineering with applied AI to create intelligent systems used by millions, worldwide. She received national innovation awards and was recognized as a Best Manager during her tenure.

About the Technical Reviewer

Amanpreet Singh Bhogal is an AI Engineer specializing in Machine Learning (ML), Deep Learning (DL), and Model Context Protocol (MCP). His work focuses on designing and deploying scalable AI systems that bridge the gap between research and real-world applications. With expertise in building ML pipelines, fine-tuning models, and orchestrating AI workloads on modern infrastructure, Amanpreet ensures that all his solutions are both technically robust and business-driven.

He has collaborated with cross-functional teams to deliver AI-enabled automation, decision-making, and digital transformation initiatives. By leveraging MCP, Amanpreet emphasizes structured and contextualized AI workflows that enhance adaptability, precision, and efficiency. His strong background in cloud-native development and orchestration further strengthens his ability to deliver end-to-end AI solutions at scale.