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Vector Databases & Semantic Search
Master the infrastructure layer of modern AI: vector databases, ANN algorithms, embedding strategies, and production semantic search systems at scale.
10 hours8 modules3 projects
$22/mo
$49/moor $230 lifetime $799
- Access to all 12 courses
- All future updates
- Certificate of completion
- 30-day money-back guarantee
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.
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?
AI Infrastructure Engineers
Designing the data layer for production AI applications at scale
Search Engineers
Upgrading legacy keyword search systems with semantic capabilities
RAG Practitioners
Want to go deeper on the retrieval layer beyond what RAG courses cover
Prerequisites
- RAG: Build Knowledge-Powered AI
- Python for AI
Tools & Technologies
PythonPineconeWeaviateQdrantpgvectorPostgreSQL