Vector Database Foundation is a focused, one-day course in vector embeddings and vector databases. These form the data infrastructure layer beneath modern semantic search, recommendation systems, and RAG applications. Attendees get hands-on practice throughout. First, they see how vector embeddings represent unstructured data, such as text, images, and audio, as numerical vectors. Then they learn how similarity search finds related items efficiently at scale. It relies on Approximate Nearest Neighbor algorithms instead of exhaustive comparison. By the end, attendees can evaluate and apply vector search technology to real infrastructure decisions.
The course covers vector embeddings and similarity metrics in detail, including cosine similarity, dot product, and Euclidean distance. Attendees compare how each metric suits different embedding types and use cases. From there, they dig into Approximate Nearest Neighbor search. This includes why exact search becomes impractical at scale. Algorithms such as HNSW and IVF trade a small amount of accuracy for dramatic speed gains in exchange.
The course then closes with a product-agnostic survey of the vector database landscape. This covers the tradeoffs between managed and self-hosted options. It also covers metadata filtering, which combines vector similarity with structured queries. Together, these narrow results by attributes such as date, category, or permissions. Through lecture and lab work, attendees build a real vector search implementation. This gives them practical experience with embeddings, similarity search, and filtering. By completion, attendees have the foundational skills to evaluate and apply vector databases in RAG pipelines, semantic search, and other AI-integrated applications.
Who Should Attend
Data Scientists, ML Engineers, Developers, Data Engineers, AI Engineers
What Attendees Will Learn
Upon completing Vector Database Foundation, attendees will be able to:
- Explain Vector Embeddings and how they represent unstructured data numerically
- Measure Similarity using distance metrics such as cosine similarity and Euclidean distance
- Search Vectors Efficiently using Approximate Nearest Neighbor algorithms
- Select Vector Databases appropriate to specific application requirements
- Apply Metadata Filtering to combine vector search with structured queries
Prerequisites
No formal prerequisites are required for this course; basic programming familiarity is helpful but not required.