Vector Database Systems Engineering by Colton Zhao (9 results)
- Softcover
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- Softcover
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- Softcover
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- Softcover
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- Softcover
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- Softcover
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- Softcover
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Paperback. Condition: new. Paperback. Vector Database Systems Engineering is an advanced, practical guide to building intelligent, scalable, and high-performance vector-native architectures for modern AI applications, this book explores the core principles, engineering patterns, and production methodologies behind vector search,… embedding management, and semantic retrieval.You'll learn how to evaluate and implement vector databases, optimize similarity search, design hybrid indexing structures, and build large-scale retrieval-augmented generation (RAG) pipelines for real-world applications. From embeddings lifecycle management to distributed storage, from latency optimization to multi-model retrieval routing, this book explains how to construct robust AI data systems that support search, reasoning, and generative intelligence.Packed with detailed architectural breakdowns, hands-on examples, performance benchmarks, and infrastructure blueprints, this guide empowers you to make informed decisions about vector database technologies, system capacity planning, replication strategies, and long-term AI data governance. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Language: English
Published by Independently published, 2026
- Softcover
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Seller: California Books, Miami, FL, U.S.A.California Books
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- Softcover
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Seller: CitiRetail, Stevenage, United KingdomCitiRetail
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Paperback. Condition: new. Paperback. Vector Database Systems Engineering is an advanced, practical guide to building intelligent, scalable, and high-performance vector-native architectures for modern AI applications, this book explores the core principles, engineering patterns, and production methodologies behind vector search,… embedding management, and semantic retrieval.You'll learn how to evaluate and implement vector databases, optimize similarity search, design hybrid indexing structures, and build large-scale retrieval-augmented generation (RAG) pipelines for real-world applications. From embeddings lifecycle management to distributed storage, from latency optimization to multi-model retrieval routing, this book explains how to construct robust AI data systems that support search, reasoning, and generative intelligence.Packed with detailed architectural breakdowns, hands-on examples, performance benchmarks, and infrastructure blueprints, this guide empowers you to make informed decisions about vector database technologies, system capacity planning, replication strategies, and long-term AI data governance. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.
