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CuratedBeginner$20
Vector Databases & Embeddings
Understand embeddings, distance metrics, ANN indexes, and the vector database landscape. Design end-to-end vector search pipelines.
Modules
6
Verified
0
In Progress
0
What you walk away with
- ✓A shareable portfolio URL with your project walkthrough
- ✓Module-by-module timeline of everything you built
- ✓All the code — pipelines, guardrails, deployment configs
- ✓Production patterns documented on your profile
Claude CodeTypeScriptJSON
Build a deep understanding of vector search from the ground up. You'll learn what embeddings are and how they capture meaning, trace the tokenize-encode-pool pipeline, compare 8 embedding models on quality and cost, master HNSW and IVF indexing algorithms, evaluate 6 vector databases (pgvector, Pinecone, Qdrant, Weaviate, Chroma, Milvus), design hybrid search pipelines with re-ranking, and build an end-to-end vector pipeline for a real support knowledge base.