Additional information
by Duy V. Nguyen (Author)
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.
Table of Contents
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
