Why Feature Stores Didn't Fix Training–Serving Skew

Training–serving skew remains a prevalent issue in production machine learning (ML), with feature stores failing to fully address the problem. The root cause of skew is identified as the movement of features across system boundaries, which alters execution context and leads to inconsistent data behavior despite matching code.

This development highlights the limitations of feature stores in solving ML challenges, emphasizing the need for a deeper understanding of the execution layer and the dynamics of data movement to ensure reliable model performance in production environments.