This workflow shows how to use the Weak Label Model Learner and Predictor nodes to aggregate sources of weak supervision such as weak models or simple rules into a single strong source that can be used to train various models. The models trained here are Gradient Boosted Trees, Logistic Regression and Deep Learning. In the final step of the workflow these models are applied to unseen data and the results are visualized with the Binary Classification Inspector that allows to compare the performance of different models in an interactive view. The data used is a preprocessed version of the Adult dataset (https://archive.ics.uci.edu/ml/datasets/Adult)