Reliable AI at Scale: Data Engineering for the Intelligent Systems Era — Sample Chapters (Chapters 1 and 4)

Abstract

This free sample edition contains two full chapters of "Reliable AI at Scale: Data Engineering for the Intelligent Systems Era," a book about the data systems artificial intelligence actually stands on. The book's argument: AI systems are data-intensive systems, and their failures are data failures. Included chapters: - Chapter 1: The Data-Intensive AI Stack — the book's thesis and the seven-layer frame (ingestion, storage, processing, orchestration, quality, observability, authority) every later chapter deepens. - Chapter 4: Stream Processing and Real-Time ML — event time, watermarks, and exactly-once semantics, then the ML turn: real-time features, point-in-time correctness, and training-serving skew. The full book has eleven chapters: 1. The Data-Intensive AI Stack 2. Ingestion at Scale: From Events to Actions 3. Storage for Intelligent Systems 4. Stream Processing and Real-Time ML 5. Consistency, Correctness, and Data Quality 6. Feature Engineering and Feature Platforms 7. Orchestration, Sagas, and Long-Running Workflows 8. Federated and Privacy-Preserving Learning 9. MLOps, Feedback Loops, and Continuous Intelligence 10. Autonomous Agents as Data-Intensive Systems 11. Operating AI Systems in Production: Reliability, Observability, Security Each chapter is written to stand alone: an opening production-failure scenario (a fictional composite), the principles that explain it, the architectural patterns that prevent it, trade-offs, and a "what to build first" checklist. About the author: Sai Prasad Veluru is a Senior Software Engineer at Immihub, where he works on distributed systems and production AI. Over eleven years in the industry — including engineering roles at Apple, JPMorgan Chase, and Visa — he has designed and operated high-throughput ingestion platforms, real-time streaming systems, and cloud-native data infrastructure, and he now builds agentic AI systems engineered to survive production. He is the author of twenty-five publications on distributed systems, stream processing, federated learning, and production AI, cited more than 150 times, and he serves as a judge at HackMIT and MHacks. LinkedIn: https://www.linkedin.com/in/saiprasadveluru © 2026 Sai Prasad Veluru — shared freely for reading; all rights reserved.