Problem
Scenario
You are building an ML powered ranking system for a consumer product feed. Some predictions can be precomputed ahead of time, while others depend on fresh user context and must be computed at request time. You need to decide how to split inference between batch and online paths.
Question
What are the trade-offs between online and batch serving for ML models?
What this tests
- Choosing between batch and online inference by feature volatility
- Designing a retrieval → ranking → re-ranking stack
- Using a feature store to reduce training-serving skew
- Handling feature drift, fallback paths, and monitoring
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