Understanding vector databases with real-world examples
About
Modern AI runs on meaning, not just data. In this explainer, Brian Sam-Bodden unpacks what vector databases are and why they’re essential for unstructured data like images, text, and video. Learn how concepts like vector embeddings, cosine similarity, and HNSW indexing make search, recommendations, and AI pipelines more intelligent and efficient.
15 minutes
Key topics
Understand the shift from structured to unstructured data
Learn how ML models create and use vector embeddings
See how cosine similarity and HNSW indexing power fast, accurate search
Explore how real-world systems use vector databases for smarter AI
