Vector Databases & Semantic Search
Master the infrastructure layer of modern AI: vector databases, ANN algorithms, embedding strategies, and production semantic search systems at scale.
About This Course
Vector databases are the memory layer of modern AI applications. This course goes deep on the technology — approximate nearest neighbor algorithms, index structures, scaling strategies, and the practical engineering of semantic search systems that handle millions of vectors in production. You'll work hands-on with Pinecone, Weaviate, Qdrant, and pgvector. Before diving in: ANN (Approximate Nearest Neighbor) algorithms find the closest matching vectors without scanning every single one — trading a small accuracy loss for massive speed gains. HNSW and IVF are two common index structures that make this possible, and both are explained visually from scratch in module 2. This is an infrastructure-engineering course best suited to developers who have already built a RAG system and want to understand what's happening under the hood. Not sure if you need this depth? If you can ship your product using Pinecone or another managed service as a black box, you do not need this course yet — the RAG course covers enough for most production use cases. Come here when you need to choose your own vector database stack, optimize for scale, debug retrieval performance, or design multi-tenant data isolation.
What You'll Learn
- Understand ANN algorithms: HNSW, IVF, PQ, and when to use each
- Design and manage vector indices for production workloads
- Implement multitenancy and namespace isolation in vector DBs
- Optimize query performance with metadata filtering and index tuning
- Build hybrid search systems combining dense and sparse retrieval
- Scale vector databases to handle hundreds of millions of vectors
- Monitor vector DB performance and diagnose query bottlenecks
- Choose between Pinecone, Weaviate, Qdrant, and pgvector for your use case
Who Is This For?
Designing the data layer for production AI applications at scale
Upgrading legacy keyword search systems with semantic capabilities
Want to go deeper on the retrieval layer beyond what RAG courses cover
Prerequisites
- RAG: Build Knowledge-Powered AI
- Python for AI