Feature serving is the process of exporting or fetching feature values for training or inference. In Vertex AI, there are two types of feature serving---online serving and offline serving. Online serving retrieves the latest feature values of a subset of the feature data source for online inferences. Offline or batch serving exports high volumes of feature data---including historical data---for offline processing, such as ML model training.