A phase of the machine learning lifecycle during which data scientists apply an ML algorithm to historical data to generate the desired model accuracy. This is an iterative process in which they review model results and then adjust features, tweak model parameters such as weights, or change ML techniques or algorithms, as needed.
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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.
- Kevin Petrie in The Machine Learning Lifecycle and MLOps: Building and Operationalizing ML Models - Part II
May 12, 2021 (Blog)
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