{"id":14574,"date":"2026-08-10T11:15:46","date_gmt":"2026-08-10T11:15:46","guid":{"rendered":"https:\/\/nokobox.com\/index.php\/item\/udemy-vector-databases-fundamentals-to-production-2026-edition-2026-4\/"},"modified":"2026-08-10T11:15:46","modified_gmt":"2026-08-10T11:15:46","slug":"udemy-vector-databases-fundamentals-to-production-2026-edition-2026-4","status":"publish","type":"digital_item","link":"https:\/\/nokobox.com\/index.php\/item\/udemy-vector-databases-fundamentals-to-production-2026-edition-2026-4\/","title":{"rendered":"Udemy \u2013 Vector Databases Fundamentals to Production [2026 Edition] 2026-4"},"content":{"rendered":"
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Descriptions<\/h2>\n

Vector Databases Fundamentals to Production [2026 Edition], In the era of AI-powered applications, vector databases are the foundation of every RAG pipeline, semantic search system, and intelligent application. This comprehensive course takes you from fundamentals to production deployment with the three databases that matter in 2026: Pinecone, Chroma, and pgvector. You will understand how vector databases work, why they outperform traditional databases for AI applications, and the mathematics behind embeddings and similarity search. The course covers mastering three leading databases: Chroma for prototyping, Pinecone as a managed cloud solution, and the newly added pgvector for production deployments. You will also learn advanced chunking strategies, including fixed, recursive, and semantic chunking, and implement hybrid search by combining BM25 keyword search with vector similarity for better retrieval accuracy. The curriculum is fully updated for April 2026, with all code working with current APIs and modern LangChain LCEL patterns.<\/p>\n

This course emphasizes a real-world focus, covering production costs, scaling decisions, and infrastructure trade-offs that tutorials often skip. Through hands-on projects, you will build working RAG pipelines, semantic search systems, and hybrid retrieval solutions. With over 8 hours of new and updated content, you will learn to tune HNSW index parameters, analyze real infrastructure costs, and use a decision framework with 9 concrete scenarios to choose the right database for your use case. This course is designed for developers building RAG applications, data scientists adding semantic search to existing systems, and engineers evaluating vector database options for production environments. Basic Python programming and familiarity with APIs are required, but no machine learning background is necessary as the mathematical concepts are explained intuitively.<\/p>\n

What you\u2019ll learn<\/h3>\n