rubicon-ml is a data science tool that captures and stores model training and execution information, like parameters and outcomes, in a repeatable and searchable way. Its git integration associates these inputs and outputs directly with the model code that produced them to ensure full auditability and reproducibility for both developers and stakeholders alike. And while experimenting, the dashboard makes it easy to explore, filter, visualize, and share recorded work.

Visit the glossary to explore the terminology.


rubicon-ml is composed of three parts:

  • A Python library for storing and retrieving model inputs, outputs, and analyses to filesystems that’s powered by fsspec

  • A dashboard for exploring, comparing, and visualizing logged data built with dash

  • And a process for sharing a selected subset of logged data with collaborators or reviewers that leverages intake

To see each of these parts in action, visit the Quick Look!


Use rubicon_ml to capture model inputs and outputs over time. It easily integrates into existing Python models or pipelines and supports both concurrent logging (so multiple experiments can be logged in parallel) and asynchronous communication with S3 (so network reads and writes won’t block).

Meanwhile, periodically review the logged data within the dashboard to steer the model tweaking process in the right direction. The dashboard lets you quickly spot trends by exploring and filtering your logged results and visualizes how the model inputs impacted the model outputs.

When the model is ready for review, rubicon-ml makes it easy to share specific subsets of the data with model reviewers and stakeholders, giving them the context necessary for a complete model review and approval.