How Feature Registries and Governance Can Prevent Common ML Failures

Machine learning teams frequently suffer from training-serving skew, data leakage, and duplicate features due to weak governance and no single source of truth for feature definitions. A well-designed feature registry addresses these issues by storing key metadata such as ownership, versioning, compute logic, and lineage, enabling engineers to evaluate and reuse features quickly. Dual storage architecture — offline for training and online for low-latency serving — ensures consistency between what models train on and what they encounter in production. Experts caution that governance should remain lightweight, as overly rigid review processes can slow experimentation and push teams toward untracked workarounds. A practical registry schema includes fields for sensitivity tagging, freshness SLAs, validation suites, and usage metrics, making feature discovery both safe and efficient.