Modern Vector Systems: Build AI-Ready Infrastructure with Cutting-Edge Vector Databases for Real-Time Search and Discovery

Huie, Gilbert

ISBN 13: 9798289025029

Editore: Independently published, 2025

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Dati bibliografici

• Titolo: Modern Vector Systems: Build AI-Ready Infrastructure with Cutting-Edge Vector Databases for Real-Time Search and Discovery
• Editore: Independently published
• Data di pubblicazione: 2025
• Lingua: Inglese
• ISBN 13: 9798289025029
• Rilegatura: Brossura
• Condizione: New

Descrizione articolo

Riassunto

Unlock the next generation of search and AI infrastructure with Modern Vector Systems: Build AI-Ready Infrastructure with Cutting-Edge Vector Databases for Real-Time Search and Discovery. Whether you’re an AI engineer, backend developer, data architect, or researcher, this book equips you with a deep, practical understanding of how to build scalable, intelligent systems using vector databases and embedding-based retrieval.
In today's world of large language models, semantic search, and real-time personalization, the limitations of keyword-based systems are more apparent than ever. Vector search is now the backbone of modern applications—from personalized recommendations and fraud detection to RAG pipelines that power chatbots with memory. This book demystifies the architecture, tools, and techniques needed to operationalize vector-based search and retrieval in production systems.
You’ll learn how vector embeddings work across text, images, audio, and code, and how to use them with popular vector databases like FAISS, Weaviate, Milvus, Qdrant, and Pinecone. The book walks you through key components of vector-powered systems, including indexing algorithms, similarity metrics, hybrid retrieval strategies, observability, security, and best practices for scaling.
You’ll also explore advanced topics like:

• Connecting vector stores to LLMs for Retrieval-Augmented Generation (RAG).
• Designing efficient embedding pipelines and managing drift over time
• Tuning performance for low latency, high throughput applications.
• Ensuring access control, compliance, and governance in sensitive deployments.
• Applying vector search in real-world use cases like semantic product discovery, content filtering, and recommendation engines

With practical code snippets, architecture diagrams, and real-world examples, this book bridges the gap between foundational concepts and production-ready systems. It doesn’t just explain what vector systems are—it shows you how to build and maintain them effectively.
If you're ready to build AI-ready infrastructure that can search, understand, and adapt—this is the book you’ve been looking for. Take the leap and future-proof your systems—scroll up and grab your copy now.