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Feature stores play a critical role in MLOps by centralizing feature engineering, storage, and reuse across different ML models. One major challenge is ensuring consistency between online (real-time) and offline (batch) feature stores to prevent data discrepancies that can degrade model performance.

Another challenge is scaling feature computation efficiently while maintaining low latency for real-time predictions. This requires a well-architected infrastructure that supports caching, indexing, and efficient retrieval of features. Additionally, organizations must manage versioning, governance, and access control to ensure secure and compliant feature usage across teams.

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