Problem
Scenario
You are building a machine learning system that produces predictions used in a user-facing product. Some features change continuously, while others are updated on a schedule, and the team needs to decide how predictions should be computed and delivered.
Question
How would you choose between online and batch serving for a model?
What this tests
- Choosing between online, batch, and hybrid serving
- Feature freshness and feature store design
- Latency and cost tradeoffs
- Training-serving skew and feature drift awareness
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