Ultimate Milvus Vector Database for AI Apps

Prashanth Raghu

ISBN 10: 9349887185 / ISBN 13: 9789349887183

Published by Orange Education Pvt Ltd, 2026

About this Item

nach der Bestellung gedruckt Neuware - Printed after ordering - Build Production AI Systems Using the World's Leading Vector DatabaseBook DescriptionVector 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 Contents1. Introduction to Vector Databases2. Fundamentals of Vectors and AI3. Components of Milvus4. Data, Storage, and Cluster Management5. Indexing Schemes6. Indexing Schemes Binary, Sparse, and GPU7. Distributed Data Management8. Distributed Index and Query Management9. Design of the Proxy Server10. GPU-Based Indexes and Optimizations11. Auxiliary Components12. Network ManagementIndex.

Seller Inventory # 9789349887183

Bibliographic Details

• Title: Ultimate Milvus Vector Database for AI Apps
• Publisher: Orange Education Pvt Ltd
• Publication Date: 2026
• Language: English
• ISBN 10: 9349887185
• ISBN 13: 9789349887183
• Binding: Taschenbuch
• Condition: Neu
• Dimensions: 235W x 191H x 14D millimeters 235 millimeters width by 191 millimeters height by 14 millimeters depth
• Weight: 492 grams

About this title

Synopsis

Build Production AI Systems Using the World's Leading Vector Database

Key Features
● Get a free one-month digital subscription to www.avaskillshelf.com
● Comprehensive Coverage of vector database, including embeddings, indexing, and similarity search for building intelligent AI applications.
● Build Scalable AI applications using Milvus for production workloads with engineering precision.
● Covers Distributed AI systems with GPU optimisation and enterprise deployment patterns for architecting Milvus at scale.

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.

The final sections address distributed index and query management, GPU-accelerated AI, proxy server design, and enterprise network architecture. Thus, by the end of the book, you can design and deploy scalable AI applications using Milvus with confidence, understanding both the theoretical foundations and the engineering decisions that make vector search systems reliable and performant at scale.

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.
● Architect distributed data and query management systems for large-scale Milvus deployments.
● Optimise AI inference pipelines using GPU-based indexes and hardware acceleration for maximum performance.
● Design proxy server architecture and network management for robust, enterprise-grade Milvus systems.

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

About the Author
Prashanth Raghu works at the intersection of semiconductor architecture, artificial intelligence, and foundational logic systems. As CEO of Sudarshana Semiconductors, his prime focus is on building original compute and reasoning architectures, spanning ASIC design, ML acceleration, and next-generation processing paradigms. His works emphasize first-principles thinking, long-horizon engineering, and technologies that are difficult to replicate without deep technical grounding.