Next-Gen Vector Databases: Hands-On Techniques for High-Dimensional Search, Multimodal Retrieval, and AI-Powered Applications.: 2 (Vector Database ... to Production-Ready AI Search Systems) - Tapa blanda

Sinopsis

Next-Gen Vector Databases: Hands-On Techniques for High-Dimensional Search, Multimodal Retrieval, and AI-Powered Applications is your definitive guide to building the next generation of intelligent, scalable, and production-ready vector search systems. Designed for engineers, data scientists, and AI researchers, this book takes you beyond the fundamentals and dives deep into advanced vector database architectures, cutting-edge retrieval strategies, and real-world AI applications.
In this book, you’ll explore:

• High-Dimensional Vector Spaces: Master the mathematical foundations of embeddings, distance metrics, and dimensionality reduction.
• Adaptive and Distributed Indexing: Implement HNSW, IVF, PQ, and hybrid indices for real-time, large-scale search.
• Multimodal Retrieval: Integrate text, images, audio, and video into unified vector spaces for AI-powered search.
• Neural and Retrieval-Augmented Generation (RAG): Combine vector search with LLMs to build next-level chatbots, recommendation engines, and knowledge systems.
• Edge and Federated Search: Deploy AI search pipelines across distributed environments with privacy-preserving embeddings.
• Performance, Security, and Optimization: Scale, accelerate, and secure your vector database infrastructure for production workloads.

• Advanced, code-first guidance for modern vector search systems
• Production-ready design patterns with security and compliance best practices
• Deep dive into neural retrieval, adaptive indexing, and multimodal pipelines
• Real-world use cases across search, recommendation, AI, and generative applications

• AI and ML engineers building large-scale search and recommendation systems
• Data scientists integrating vector retrieval into analytics and pipelines
• DevOps professionals deploying distributed, high-performance vector databases
• Researchers exploring retrieval-augmented generation, multimodal search, and next-gen AI applications

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