Platform Engineering for Artificial Intelligence

Artificial intelligence is reshaping modern industries, but building scalable and reliable AI systems requires more than models, rather it needs strong platforms, automation, and data-driven insights. This book addresses that critical gap by exploring the AI ecosystem through foundational architecture and infrastructure automation.

This book provides an in-depth knowledge of designing and building operating platforms that supportAI initiatives, covering data pipelines, model lifecycle management, infrastructure engineering, and operational best practices. Each chapter integrates core technical concepts and introduces generative AI, LLMs, and agentic protocols, backed by real-world case studies in healthcare and content moderation, supporting secure and cost-aware AI systems.

After reading this book, readers will gain the knowledge and foundational skills to design and build AI platforms that optimize development workflows and embrace automation. This expertise prepares the readers to lead AI-driven initiatives and deliver measurable business impact in any modern organization.

WHAT YOU WILL LEARN
● Fundamentals of platform engineering, with a focus on how they apply to AI systems.
● Design scalable data pipeline architectures.
● Optimize cloud costs using FinOps.
● Design, build, and operate secure, high-performance, and scalable ML pipelines.
● Engineer platforms to support generative AI and LLMs.
● Apply IaC and FinOps principles to manage resources and optimize costs.
● Build, scale, and lead high-performing platform engineering teams.

WHO THIS BOOK IS FOR
This book is for platform engineers, MLOps professionals, data scientists, and cloud developers who pursue designing and building scalable, efficient AI platforms. Readers should possess intermediate AI/ML knowledge and basic experience with cloud technologies, and is valuable for leaders overseeing AI platform initiatives.

1. Need for Platform Engineering in AI
2. Core Concepts of AI Platforms
3. Developing Plan for Data Pipelines
4. Architecting Data Pipelines
5. Building Modular Machine Learning Pipelines
6. Governance and Security in AI Platforms
7. Infrastructure as Code for AI Platforms
8. Financial Management in Platform Engineering
9. Operationalizing Machine Learning Models
10. Observability and Monitoring
11. Building High-performing Platform Teams
12. Managing and Scaling Platform Team
13. Scaling Platforms for Enterprise AI
14. Platform Engineering For Generative AI
15. Real-world Use Cases
16. Emerging Trends in AI Platforms

Duy V. Nguyen has more than twenty-three years of experience in software engineering, serving as a technical leader and hands-on engineer on numerous large-scale initiatives. His work spans enterprise mobile platforms, cloud and platform engineering, machine learning systems, and distributed architectures, delivered across a wide range of technology stacks.

Over the course of his career, Duy has contributed to and led successful large-scale projects at global technology companies, including IBM, Red Hat, and The Walt Disney Company. He is currently a principal software engineer at Disney, where he focuses on building scalable enterprise platform capabilities and AI-driven systems.

Duy holds a master’s degree in computer science from North Carolina State University and multiple industry- recognized professional certifications from organizations such as IBM, AWS, Databricks, The Open Group, and PMI. In recognition of his technical emminence and contributions to the engineering community, he was appointed as a member of the IBM Academy of Technology. He is a co-inventor on several patents in cloud computing, media engineering, and artificial intelligence, and a co-author of widely referenced publications on microservices, mobile platform engineering, and cloud systems.