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Design and Modeling Vector Database [#1016816]
Design and Modeling Vector Database by Ajit Singh
English | December 28, 2025 | ISBN: N/A | ASIN: B0GCWKHB67 | 350 pages | EPUB | 2.09 Mb
This book, "Design and Modeling Vector Database," provides a comprehensive, hands-on, and academically rigorous introduction to the world of vector databases. It is designed to serve as the primary textbook for undergraduate and postgraduate courses on modern database systems, AI/ML engineering, and data science.
Philosophy:
My core philosophy is "Learn by Building." I believe that the most profound understanding comes from bridging the gap between theory and practice. This book demystifies the magic behind AI-powered search and retrieval by breaking down complex systems into their fundamental components. I move beyond rote memorization of APIs and focus on the "why" behind the "how"-exploring the data structures, algorithms, and system design principles that enable vector databases to perform their remarkable feats. The content is presented in the simplest possible terms, ensuring that a student with a basic understanding of programming and data structures can confidently master the material.
Key Features
1. Beginner to Advanced: The content is scaffolded to cater to students at all levels, from those new to databases to those with existing software engineering experience.
2. Comprehensive Coverage: Covers the entire lifecycle of a vector database, from theoretical modeling and design to implementation, deployment, and future trends.
3. Vendor-Agnostic Principles: While we use popular open-source tools for examples, the core principles taught are universal and applicable to any vector database system.
4. Focus on Applications: Strong emphasis on real-world applications such as semantic search, recommendation engines, and Retrieval-Augmented Generation (RAG) for LLMs.
To Whom This Book Is For
1. B.Tech/M.Tech Computer Science Students: As a core or elective textbook for courses on Database Management Systems, AI & Machine Learning, or Big Data.
2. Aspiring AI/ML Engineers and Data Scientists: Who need to understand how to build the data infrastructure for intelligent applications.
3. Software Developers and Architects: Who want to upskill and learn how to integrate vector search capabilities into their existing or new projects.
4. Self-Taught Learners and Enthusiasts: Who are curious about the technology powering the current AI revolution.
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