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.

Key topics

1. Understand the shift from structured to unstructured data
2. Learn how ML models create and use vector embeddings
3. See how cosine similarity and HNSW indexing power fast, accurate search
4. Explore how real-world systems use vector databases for smarter AI