How would you architect a real-time feature store in Azure ML?

A real-time feature store architecture combines streaming pipelines, centralized feature management, low-latency serving infrastructure, governance, and consistency guarantees between training and inference.

Feature engineering becomes extremely complex at enterprise scale because different teams often duplicate transformation logic, creating inconsistencies between training and inference.

A modern Azure ML feature store architecture includes:

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