Project: Neural Embedding Selection Board
Build a decision board that scores candidate embedding strategies and produces a release recommendation.
What you will build
- A scoring model for retrieval quality, privacy, latency, cost, and migration burden.
- A strategy recommendation: local, managed, domain-specific, or hybrid.
- A release gate that blocks unsafe candidates.
- A migration note with re-indexing and rollback guidance.
Product-level outcome
Model choice as architecture
Your output should sound like an architecture review, not a model fan post: "Candidate B wins retrieval quality but needs privacy approval and a parallel index release before rollout."