The term "feature importance" relates to methods for scoring each input feature for a particular model; the scores indicate the "importance" of each feature. A higher score indicates that the particular feature will significantly impact the model prediction for that particular variable. All data sets and data pipelines required to productionize machine learning systems are managed with the help of feature stores.