Embedding Pipeline at Scale
Build an embedding pipeline that processes 1M+ documents efficiently. Practice batching, caching, and cost control.
Pythonsentence-transformers or OpenAI embeddingsModal or Raypgvector
About this project
Embedding generation at scale is the underrated specialty of AI engineering. This project teaches batching, caching, model selection (which embedding model is best per dollar in 2026?), and the operational details. Take a corpus (Wikipedia, ArXiv, GitHub repos) and embed it efficiently, with measurable cost and throughput.
Why build this in 2026?
Cheap, fast embeddings are how RAG products win on margin. Specialty hiring opportunity.
What you'll ship
- GitHub repo
Benchmark report (cost per million, throughput)
Comparison of 3+ embedding models
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Skills you'll practice
pythonvector databasesmachine learning