FastTrackML vs Rubicon-ML
FastTrackML
fasttrackml.ioRubicon-ML
capitalone.github.ioSide by side
| Figure | FastTrackML | Rubicon-ML |
|---|---|---|
| Price from | — | Free |
| Free plan | — | Yes |
| Free trial | — | — |
| Open source | — | — |
| Web | Yes | Yes |
| Windows | Yes | — |
| macOS | Yes | — |
| Linux | Yes | Yes |
| API | Yes | Yes |
| Self-hosted | Yes | Yes |
| Plans published | 0 | 1 |
| Spec score · ML Experiment Tracking Software | 7.0 (#26) | 8.3 (#7) |
| License | The project README says FastTrackML is licensed under Apache License 2.0. | The project is licensed under Apache License 2.0. |
| Logging | FastTrackML supports logging experiment parameters and metrics through the MLflow Python package. | The logging component organizes, stores, and retrieves model inputs and outputs using pluggable storage backends powered by fsspec. |
Plans
In their own words
FastTrackML
FastTrackML has no write-up on Specifiction yet; its figures are above.
Rubicon-ML
Rubicon-ML is a free, open-source Python package for capturing model training and execution details. It records inputs, outputs, parameters, and outcomes in searchable projects and experiments, while Git integration can attach repository origin, branch, and commit information for auditability. Its dashboard and widgets let users explore, filter, visualize, compare, and share logged work. Multiple experiments can be logged in parallel, and asynchronous S3 communication keeps network reads and writes from blocking. Storage backends include local files, Amazon S3, in-memory storage, and Weights & Biases. Integrations are provided for Git, Prefect, and Scikit-learn; RubiconPipeline can log parameters and metrics when Scikit-learn pipelines are fit and scored. Installations use pip or conda-forge. Visualization is an optional pip extra, while conda installs optional extras by default. The dashboard is a Dash app that can be dockerized, and remote logging does not require a hosted server. It is licensed under Apache License 2.0.