Managing a Feature Engineering Pipeline in Training and Inference

Feature engineering is one of the most pivotal steps in any machine learning project, as it transforms raw data into a structured form that can be effectively used by machine learning models. This process involves creating new features, selecting relevant ones, and manipulating data in a way that enhances the predictive power of the model. The quality of feature engineering can make or break a model’s performance, and it often requires a deep understanding of both the data and the domain.

In production environments, consistency between feature engineering during the training and inference stages is critical. Any misalignment in how features are engineered across these two stages can lead to significant performance degradation. For instance, if the model is trained with one set of transformations or feature logic but a different approach is applied when making predictions, the model’s accuracy, reliability...