Features are individual independent variables that act as inputs in a machine learning system. Features are properties of a problem for which we would like to predict results. In simplistic terms, one column of a data set can be considered to be one feature. In a more real-world scenario, you would obtain training features from existing features using a method known as “feature engineering.”
Model-driven organizations are beginning to store features centrally in what has recently been termed a feature store. A feature store is a data management layer (the output of a data lake) that allows data scientists and data engineers to share and discover features.
Feature stores enable highly curated and consistent training datasets for machine learning. This layer aids in implementing full traceability along with compliance and scalability from data source to final outcome. The term was originally coined by Uber with the introduction of its Michelangelo machine learning platform.