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$20

Vector Databases & Embeddings

Understand embeddings, distance metrics, ANN indexes, and the vector database landscape. Design end-to-end vector search pipelines with hybrid retrieval and evaluation.

"For this 500-article support KB, I'd use pgvector with hybrid search and RRF — here's the full pipeline design and cost analysis"

4 Interactive Sessions

Short, interactive sessions — watch it work, steer it, then build it yourself. Go deeper anytime with the full code walkthrough.

  1. 1

    See how AI understands meaning, not words

    Meaning becomes a place — similar things land close, and search is just finding the nearest dots.

  2. 2

    How text becomes a vector

    An embedder turns any text into one vector in three moves: tokenize, encode in context, pool — and you pick which embedder to use.

  3. 3

    Storing & searching millions

    At scale you can't check every vector — an index finds the nearest ones fast (trading a little recall for huge speed), and a database stores & serves them.

  4. 4

    Make retrieval actually work — then design yours

    Raw nearest-neighbor isn't enough — top-K, hybrid, re-rank, MMR, and evaluation make it good. Then design a real pipeline and pick its home.

Production patterns you'll master

Embedding PipelinesANN IndexingHybrid SearchRe-rankingRetrieval Evaluation

Synthetic data included

  • Distance metrics (JSON)
  • Embedding model comparisons
  • Index benchmarks at scale
  • Vector DB cost models
  • Retrieval pattern evaluations
  • Support KB scenario

What you walk away with

Shareable portfolio

A public URL showing your module timeline, patterns mastered, and completion status.

All the code

Download everything as a ZIP — pipelines, guardrails, deployment configs. Yours forever.

Module walkthrough

Each module documented with deliverables and the production pattern you implemented.

Ready to build your vector databases & embeddings?

First course free. $20 per course after that.