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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"

6 Modules

Each module builds on the previous one. By the end, you have a complete production system.

  1. 1

    What Are Embeddings?

    Understanding of vectors, similarity, and distance metrics

  2. 2

    How Embedding Models Work

    Tokenize-encode-pool pipeline and model selection

  3. 3

    Vector Database Fundamentals

    ANN indexes, trade-offs, and tuning parameters

  4. 4

    The Vector DB Landscape

    Database comparison and cost modeling

  5. 5

    Search & Retrieval Patterns

    Hybrid search, re-ranking, MMR, and evaluation

  6. 6

    Your First Vector Pipeline

    End-to-end pipeline design with decision memo

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.