Automated machine learning solutions can help address problems with data-driven activities by selecting optimal machine learning models and techniques without requiring deep machine learning expertise. Vitriol is one such solution that uses meta-learning to leverage knowledge from previous learning tasks to select imputation methods, models, and hyperparameters for new problems. It has a web application interface that allows users to easily connect databases, preprocess and complete data, select modeling tasks, and visualize results without training or space limitations. With each new problem solved, Vitriol's meta-learner continues to improve its model selection abilities.