IntroductionDeep learning (DL) has emerged as a powerful tool to solve a variety of complex problems that have been difficult to solve with traditional methods. However, domain experts attempting to apply  DL methodology have to learn to code in order to use it. Numerous frameworks have been developed, such as TensorFlow, that simplify the task of building and training complex DL models, yet their efficient use requires a good working knowledge of Python language.  Consequently, a variety of tools have been developed that provide easier to use DL models, ranging from the Keras API built on top of TensorFlow that still requires coding, to tools such as H2O that provide a point-and-click web-based interface to configure and train pre-built models. Among this new breed of tools, IBM Visual Insights (formerly IBM PowerAI Vision) \cite{120} has taken this concept further by providing a web-based graphical user interface (GUI) for configuring and training a variety of models, as well as tools and APIs for deploying these models on a variety of platform. The tool empowers domain experts without any knowledge of coding or underlying hardware to take advantage of complex DL models trained on large datasets.  In this short article, we walk through an example of using  IBM Visual Insights to train a DL model on a  chest X-ray image dataset.