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Excitement around the transformative potential of machine learning in health care belies a reliance on deep technical expertise that leaves this technology in the hands of the few. Typically, a practitioner of machine learning undertakes numerous tasks in the process of training and testing a model for classification. The process requires substantial technical knowledge and—perhaps somewhat incongruously—is often both highly detailed and loosely defined. In The Lancet Digital Health, Livia Faes, Siegfried Wagner, and colleagues1 report on their experience of using a service that creates an abstraction from the training and testing process, enabling a professional with no coding experience to build a model that might once have been out of reach. In the study, the authors train models for classifying disease in medical images using Cloud AutoML, a service that requires minimal technical knowledge. Discriminative …