What are the key architectural considerations when designing a feature store for a large-scale ML platform? How do you ensure consistency between training and serving?

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A well-designed feature store addresses several critical requirements for ML platforms at scale. The first consideration is the dual-mode serving requirement. Training requires batch access to historical feature values at specific timestamps.