AI Foundations & Concepts · Practitioner
Embeddings & Vector Databases in Practice
Understand embedding spaces, similarity metrics, ANN indexes, and how to choose and operate a vector store.
- 50 min lesson
- intermediate level
- Engineering Track
- Narrated presentation
What you’ll learn
- Embedding models and vector space semantics
- Cosine similarity and normalisation
- ANN indexes: HNSW, IVF, product quantisation
- Recall@k and index tuning
- Metadata filtering and namespaces
- Re-indexing on embedding model upgrades
Inside this lesson
A narrated, slide-by-slide presentation with 1 prediction exercise, 1 knowledge check, 4 videos. You answer as you go, and the platform checks your work.