About This Course
Vector databases are the backbone of modern AI applications, enabling semantic search, recommendation systems, and RAG pipelines. This course covers everything from the theory of vector embeddings to deploying production vector search systems.
You will learn about vector embeddings, similarity metrics, indexing algorithms (HNSW, IVF), and hands-on implementation with Pinecone, Weaviate, ChromaDB, and pgvector. The course covers data ingestion pipelines, query optimization, and scaling strategies.
Projects include building a semantic search engine, a recommendation system, a duplicate detection system, and a RAG pipeline with hybrid search. By the end, you will know which vector database to choose for your use case and how to optimize it for production workloads.
