Feature Store Integration

You work as a machine learning engineer at a fintech company. You are tasked with designing a system to standardize feature storage and retrieval for all production ML models that predict credit risk. The system must ensure consistent access to customer transaction data, account histories, and real-time financial events, while supporting rapid model deployment using AWS SageMaker.

How would you architect a feature store and integrate it into the end-to-end ML pipeline on AWS SageMaker?