Once the data scientist selects their ML technique, they “train” the algorithm—potentially multiple versions of it—on one or more historical datasets to create the actual model. ML engineers, who serve a dual role of developer and data scientist, also might assist data scientists with the training process. Let’s first examine training for supervised ML, which has labeled outcomes. “Training” in this case means that the data scientist and ML engineer apply the algorithm to combinations of historical features and outcomes (a.k.a. labels) so that it can learn the relationship between them. As it learns, the algorithm generates a score hat predicts, classifies, or prescribes outcomes of features. The data scientist compares those predicted outcomes to real historical outcomes, then makes changes to improve accuracy, with the help of the ML engineer and data engineer.