A lot of machine learning models perform well in development but fail once they reach production. The issue usually is not the model. It is the inconsistency between training data and live data. When features are defined one way during training and another way in production, results become unreliable. Teams end up spending more time fixing pipelines than improving the system. The Overlooked Problem With Features Features are the foundation of any AI system, yet they are often handled in a fragmented way. Different teams recreate the same features, definitions drift over time, and there is no single source of truth. This leads to duplication, confusion, and slower development cycles. It also increases the risk of errors that are difficult to trace. How Feature Stores Fix the Core Issue Feature stores provide a centralized way to manage features across the entire machine learning lifecycle. They ensure that the same feature logic is used in both training and production environments.…