Designing AI-Intensive Applications: Scalable Data Architectures for Machine Learning Pipelines, Vec

Most AI books stop at the model. This one starts where models need to live: in production, under real load, with real data, and real consequences for failure. Designing AI-Intensive Applications covers the full infrastructure stack that separates impressive AI demonstrations from reliable AI products-from the streaming pipelines and feature stores that feed models with high-quality data, through the vector databases and RAG systems that give models access to knowledge, to the LLM serving clusters and agentic frameworks that deploy AI capabilities at scale.

Written by a principal AI infrastructure architect with sixteen years of production experience, this book provides concrete architectural patterns, operational disciplines, and engineering principles drawn from systems serving hundreds of millions of users. You will learn how to design feature stores that eliminate training-serving skew, build vector databases that serve billions of embedding queries, architect RAG systems that retrieve with precision and generate with accuracy, scale LLM serving clusters that handle thousands of concurrent inference requests, implement MLOps pipelines that keep models continuously improving, and operate AI systems that remain reliable when components fail.

Whether you are building your first AI-powered product or scaling an existing AI platform, this book gives you the technical depth and practical wisdom to build AI systems that actually work-not just in demonstrations, but in production, at three in the morning, when everything matters.

Topics covered include: data lakehouse architecture, Apache Kafka and Flink for streaming AI, feature stores and point-in-time correct retrieval, distributed ML training, model serving with vLLM and Triton, embeddings and vector search, HNSW and IVF index structures, RAG system design and evaluation, real-time AI architecture, LLM inference optimization, agentic AI systems, MLOps and CI/CD for ML, model drift detection, fault tolerance and graceful degradation, horizontal and vertical scaling, and responsible AI architecture.