System designHardsd-g097

Subject EmbeddingsLevel Senior–Staff~45 minCommon in ML systems interviewsIndustries Technology, Software development

Question

An e-commerce semantic-search system has 300M product embeddings serving live traffic, and the ML team wants to upgrade the embedding model to a better one. The new model produces vectors in a different space, so old and new embeddings are not comparable — you cannot mix them in one index. You must migrate 300M items to the new embedding without taking search down and without a window where results are garbage. Design the embedding-versioning and zero-downtime re-embedding migration.

What a strong answer looks like

Clarify scale and constraints first. Propose a clean component breakdown, then go deep on the hard parts — data model, bottlenecks, consistency, failure modes — and name the trade-offs you are making.

Learn the concepts

Narrate your design

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Run or narrate your approach, then ask the coach.