Database Design & Modeling for Artificial Intelligence

De: Ajit Sungh

Narrado por: Virtual Voice

This book provides a comprehensive, practical, and modern exploration of designing and modeling databases specifically for Artificial Intelligence applications. It systematically connects the dots between foundational data principles and the sophisticated data requirements of AI, Machine Learning, and Deep Learning systems.


Philosophy:

The core philosophy of this book is "Application-Driven Learning." I believe that the abstract concepts of database architecture and data modeling become clear and meaningful only when they are tied to a tangible purpose. In this context, that purpose is to build powerful, efficient, and scalable AI systems. We will demystify complex topics by breaking them down into simple, understandable components and immediately illustrating them with real-world AI-centric scenarios, such as building recommendation engines, powering semantic search, or managing data for autonomous vehicles.


Key Features:

1. Globally Compatible Curriculum: The content covers topics universally found in advanced computer science syllabi across the world.
2. Coverage: Includes relational, NoSQL (Document, Key-Value, Column-Family, Graph), and specialized AI databases (Vector databases).
3. Full Lifecycle Perspective: Covers the entire data lifecycle from design, modeling, and implementation to deployment, MLOps integration, and governance.
4. Complete Capstone Project: A fully explained, working capstone project with code to provide a real-world development experience.
5. Focus on Modern Technologies: In-depth discussion of modern data architectures like Data Lakes, Lakehouses, and cloud-native database services.


To Whom This Book is Addressed:

This book is primarily intended for:

1. B.Tech and M.Tech Computer Science/IT Students: As a core or elective textbook that perfectly aligns with modern data-centric curricula.
2. Aspiring AI/ML Engineers and Data Scientists: To build the essential data backend skills required for a successful career in AI.
3. Software Developers and Architects: Who need to upskill and learn how to design data systems for intelligent applications.
4. Database Administrators (DBAs): Who wish to transition their skills to the world of AI and Big Data.
5. University Faculty and Educators: As a comprehensive resource for teaching courses on advanced database systems, Big Data, or AI infrastructure.