The machine learning lifecycle is complex and consists of many different stages; one of the most important being the model training process. Model developers must iterate fast in order to efficiently produce the best possible results. Experiment tracking and documentation for analysis, governance, and eventual model approval must not be tedious and time consuming. Rubicon is an open source data science tool that seamlessly captures and stores model training and execution information, like hyperparameters and outcomes, in a repeatable and searchable way. Rubicon’s git integration associates experiments directly with the model pipeline code to ensure full auditability and reproducibility for both developers, reviewers, and stakeholders alike. Rubicon offers an integrated dashboard that makes it easy to explore, filter and visualize experiments. Rubicon also exposes a process for highlighting and sharing experiments of interest with collaborators and reviewers.