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Design Online and Batch ML Serving

HardSystem Design00:00
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Problem

Scenario

You are building an AI voice platform with personalization across discovery, voice selection, and content recommendations. Some predictions must react to fresh user behavior, while others can be precomputed and served cheaply.

Question

How do you design online versus batch serving for an AI product?

What this tests

  • Choosing between batch precomputation and online inference
  • Designing retrieval, ranking, and re-ranking stages
  • Using a feature store to avoid training-serving skew
  • Handling feature drift, cold start, and fallback paths