What is online / offline skew in machine learning?

The challenge of real-time machine learning lies in bridging the gap between the online and offline environments, where models are trained on historical data but need access to real-time feature values for predictions. This "online / offline skew" problem can lead to poor model quality if not addressed, as models may be trained on outdated or inaccurate features. To reduce skew, teams can use feature stores and platforms that address the different sources of skew, including differences in online and offline feature logic, historically accurate training data generation, and changes in real-world distributions. A well-designed feature platform can help alleviate these challenges, allowing teams to focus on delivering business value with their real-time machine learning systems.