Multi-task learning, or training a single model on multiple tasks, is becoming a standard tool for the modern ML practioner (see Ruder's survey from 2017 for a nice overview). It often leads to computational gains (one model performing many tasks takes up less memory and storage) as well as performance gains (learning to do well on a related auxiliary task can improve the model's ability on the primary task). While the primary purpose of the Snorkel project is to support training data creation and management, it also comes with a PyTorch-based modeling framework intended to support flexible multi-task learning (e.g. slice-aware models). In this tutorial, we introduce the basic interfaces and flow of multi-task learning tools within Cerbero.