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SKU/Artículo: AMZ-B0FX1RMG23
Vector Databases in Production : A Practical Guide to Building, Scaling, and Optimizing High-Performance RAG and Semantic Search Systems.
Format:
Kindle
Detalles del producto
Disponibilidad:
Fuera de stock
Fuera de stock
Peso con empaque:
0.36 kg
0.36 kg
Devolución:
No
No
Condición
Nuevo
Nuevo
Producto de:
Amazon
Amazon
Viaja desde
USA
USA
Sobre este producto
- Your AI Prototype is Impressive. But Will It Survive the Brutal Reality of Production? You've built a chatbot that can answer questions about your documents. You've created a search engine that understands meaning, not just keywords. The potential is enormous, but a nagging question remains: how do you move this from a clever Jupyter notebook to a scalable, low-latency, and cost-effective system that can handle millions of users and billions of vectors? The leap from prototype to production is a minefield of complexity. Without the right architecture, your application will crumble under the weight of real-world demand, plagued by slow queries, runaway cloud bills, and the dreaded "hallucinations" that destroy user trust. Vector Databases in Production is your definitive, no-fluff guide to navigating this minefield. This practical, hands-on playbook demystifies the entire lifecycle of building, scaling, and optimizing high-performance AI systems. It moves beyond the hype and provides the battle-tested engineering discipline you need to build applications that are not just intelligent, but also robust, reliable, and ready for prime time. Whether you're building a state-of-the-art RAG (Retrieval-Augmented Generation) system or a powerful semantic search engine, this book is your blueprint for success. Inside, you will learn how to:Master the Fundamentals: Go beyond theory and truly understand the core concepts of embeddings, vector space, and the fundamental trade-offs that drive every architectural decision.Navigate the Crowded Market: Get a clear, unbiased breakdown of the vector database landscape—from managed services like Pinecone and Weaviate to self-hosted powerhouses like Milvus and pgvector—and learn how to choose the right one for your use case and budget.Architect for Scale: Learn the essential patterns for designing resilient, event-driven ingestion pipelines and low-latency query services that can grow from your first vector to your first billion.Master the Core Algorithms: Peek inside the engine room and gain a practical understanding of ANN indexes like HNSW and IVF, learning how to tune them for the "iron triangle" of recall, latency, and cost.Build a Production-Grade RAG System from the Ground Up: Follow a step-by-step guide to assembling your first fact-checked, hallucination-resistant question-answering application.Engineer State-of-the-Art Search: Move beyond simple similarity. Learn advanced techniques like hybrid search, cross-encoder re-ranking, and leveraging user feedback to deliver results that are not just similar, but truly relevant.Implement "VectorOps": Apply the discipline of MLOps to your AI stack. Learn how to version your data, models, and indexes, automate quality evaluation, and perform zero-downtime deployments.This book is for:AI/ML Engineers tasked with building and deploying vector search or RAG systems.Backend and Software Developers integrating AI capabilities into new or existing applications.Data Scientists who want to understand the engineering challenges of taking their models to production.Solutions Architects designing scalable, next-generation AI infrastructure.Technical Leaders and CTOs seeking a clear framework for building reliable and cost-effective AI products.Stop building fragile demos. Start engineering production-grade intelligence. Scroll up and grab your copy of Vector Databases in Production today!
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