Ultimate Milvus Vector Database for AI Apps
Build Production AI Systems Using the World's Leading Vector Database
Book Description
Vector databases have become the critical infrastructure layer of modern AI, powering semantic search, recommendation systems, image recognition, and retrieval-augmented generation at scale. Ultimate Milvus Vector Database for AI Apps provides a comprehensive, hands-on guide to building production-grade AI applications using Milvus, the leading open-source vector database, combining mathematical foundations with practical engineering depth.
You begin with the core mathematics of AI and deep learning, then progress through the architecture of vector databases, embedding models, and similarity search APIs. The book covers how Milvus manages vector indices, handles large-scale data ingestion, and integrates with modern AI pipelines, including LLMs and generative AI workflows. Every concept is grounded in implementation, from building and training models to deploying production-ready vector search systems.
What you will learn
● Understand the mathematical foundations of vectors and AI that underpin modern intelligent applications.
● Design and build vector indices using Milvus to power accurate similarity search at production scale.
● Implement binary, sparse, and GPU-accelerated index types to optimise Milvus for diverse AI workloads.
Who is This Book For?
This book is for all AI engineers, ML practitioners, and software architects who want to build scalable AI applications using vector databases. A working knowledge of engineering mathematics, probability theory, Python, and basic database design is expected; no prior Milvus experience is required.
Table of Contents
1. Introduction to Vector Databases
2. Fundamentals of Vectors and AI
3. Components of Milvus
4. Data, Storage, and Cluster Management
5. Indexing Schemes
6. Indexing Schemes Binary, Sparse, and GPU
7. Distributed Data Management
8. Distributed Index and Query Management
9. Design of the Proxy Server
10. GPU-Based Indexes and Optimizations
11. Auxiliary Components
12. Network Management
Index
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