Vector Databases in Practice

Vector DBs in Practice is an applied course that provides a rigorous yet hands-on understanding of vector databases as core infrastructure for modern AI systems. Participants begin by demystifying embeddings and high-dimensional search, exploring similarity metrics such as L1, L2, and Cosine, and examining the key algorithms that power efficient vector retrieval at scale.

The course culminates in building Retrieval-Augmented Generation (RAG) pipelines using platforms like Pinecone, Weaviate, and ChromaDB, enabling learners to design and deploy scalable, production-ready semantic search and AI applications.