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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.
PE
HP