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Artículo: AMZ-B0FGXZVTCC
Vector Database Engineering: Building Scalable AI Search & Retrieval Systems with FAISS, Milvus, Pinecone, Weaviate, RAG Pipelines, Embeddings, High ... Equations) (AI Engineering for Practitioners)
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- Vector Database Engineering is the ultimate guide to designing, building, and deploying scalable vector search systems using tools like FAISS, Milvus, Pinecone, Weaviate, and Qdrant. Whether you're building a semantic search engine, a personalized recommendation system, or an AI-powered chatbot, this book gives you the theoretical foundations, mathematical insights, and production-ready Python code you need to succeed.What You’ll Learn Vector Embeddings & Similarity Search: Represent text, images, and data as vectors and retrieve results using cosine, Euclidean, and inner product distances. Vector Indexing at Scale: Implement FAISS HNSW, IVF, and PQ structures. Learn trade-offs between recall and latency. Managed & Distributed Databases: Use managed services like Pinecone and self-hosted options like Milvus, Weaviate, and Qdrant. Real-World Applications: Build semantic search engines, RAG pipelines, multimodal retrieval, recommendation systems, and edge deployments. Security & Compliance: Add RBAC, TLS encryption, audit logging, and GDPR-compliant deletion. Advanced Topics: Explore neural search, adaptive indexing, multimodal embeddings (e.g., CLIP), and federated search.Key Use Cases Semantic Search: Go beyond keywords using AI vector queries. Recommendations: Suggest content and products based on behavior. Multimedia Retrieval: Search images, audio, and video using embeddings. RAG: Feed live vector data into LLMs for better answers. Fraud & Anomaly Detection: Identify outliers with proximity-based search. NLP & Generative AI: Embed, retrieve, and generate content with LLMs.Why This Book? Hands-On Python: 40+ real-world examples with FAISS, Qdrant, Pinecone, Milvus, and Weaviate. Math-Based Optimization: Understand latency, memory, and performance trade-offs. Production Ready: Secure, scalable design patterns with best practices. Future Trends: Includes neural retrievers, adaptive indexing, and multimodal workflows.Who It's ForEngineers building real-time search and recommendation enginesML and Data Scientists integrating vector search in pipelinesDevOps deploying scalable and secure AI infrastructureAI researchers exploring retrieval-augmented generationStudents and builders learning practical vector searchThis is your in-depth, code-first guide to building intelligent, scalable vector database systems. Start using vector search to power the next generation of AI.Get your copy now.
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