The everyday consumer is inundated with applications powered by machine learning. But in an ordinary day, do we encounter situations and choices which could also benefit from machine learning for which there is no specific tool invented yet? We describe scenarios where people without
any machine learning background could find it useful to define their own solution which uses machine learning. Although machine learning is becoming ubiquitous, the average person is unaware of the steps involved. This abstraction makes sense, in many situations, such as traffic predictions,
it is not necessary for the driver to know what machine learning algorithm is running. However, we consider examples where knowing how to incorporate machine learning into a problem would assist in decision making. We propose a workflow with operations leading to a final application. There
are several challenges here, namely, the average consumer is not expected to have a mathematical background, nor is expected to acquire any additional background. To achieve this new utility, we use a visual analytic pipeline which integrates machine learning and the person. We use the IEEE
VAST 2018 Challenge as a case study in which the user steps through the workflow. Finally, we envision the resulting application.