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Rubicon-ML
McLean, Virginia, United States
What it is
Rubicon-ML is a free, open-source Python package for recording model training and execution details. It stores 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 a local filesystem, Amazon S3, in-memory storage, and Weights & Biases. Integrations cover Git, Prefect, and Scikit-learn; RubiconPipeline can log parameters and metrics when Scikit-learn pipelines are fit and scored. Installations are available through 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. Rubicon-ML does not require a hosted server for remote logging and is licensed under Apache License 2.0.
For and against
For
- Records inputs, outputs, parameters, and outcomes
- Git links experiments with repository and commit details
- Supports parallel and asynchronous experiment logging
- Offers local, S3, memory, and W&B backends
- Includes Prefect and Scikit-learn integrations
Against
- Weights & Biases backend is experimental
- W&B lacks project artifacts and dataframes
- Visualization requires an optional pip extra
- Requires Python package installation
The verdict
Rubicon-ML provides an open-source record of model work with flexible storage and review tools. Its W&B limitations and optional visualization dependency matter when planning an implementation.
Who it suits
Rubicon-ML is aimed at developers and science or research users tracking machine-learning experiments. It suits teams wanting local, S3, or other documented storage without a hosted server requirement.
Plans and prices
rubicon-ml
Open-source Python package · install with pip or conda
Read 9 Oct 2026 · capitalone.github.io
On the record
- Auditability
- Its Git integration associates model inputs and outputs with the model code that produced them to support auditability and reproducibility.capitalone.github.io · read 9 Oct 2026
- Concurrent and asynchronous logging
- The documentation says rubicon-ml supports logging experiments in parallel and asynchronous communication with S3 so network reads and writes do not block.capitalone.github.io · read 9 Oct 2026
- Concurrent logging
- Rubicon-ML supports logging multiple experiments in parallel and asynchronous communication with S3.capitalone.github.io · read 9 Oct 2026
- Dashboard deployment
- The dashboard is a Dash app that can be dockerized like other Dash apps.capitalone.github.io · read 9 Oct 2026
- Experiment review
- The dashboard lets users explore, filter, visualize, and share logged work with reviewers and stakeholders.capitalone.github.io · read 9 Oct 2026
- Git audit trail
- Its Git integration can record repository origin, branch names, and commit hashes alongside projects and experiments.capitalone.github.io · read 9 Oct 2026
- Git integration
- With auto_git_enabled=True, projects can record the GitHub origin URL and experiments can record the active branch and last commit hash.capitalone.github.io · read 9 Oct 2026
- Installation
- The Python package can be installed using pip or conda-forge.capitalone.github.io · read 9 Oct 2026
- Integrations
- The documentation includes integrations for Git, Prefect, and Scikit-learn.capitalone.github.io · read 9 Oct 2026
- Intended users
- The package metadata identifies developers and science or research users as its intended audience.github.com · read 9 Oct 2026
- License
- The project is licensed under Apache License 2.0.github.com · read 9 Oct 2026
- Logging
- The logging component organizes, stores, and retrieves model inputs and outputs using pluggable storage backends powered by fsspec.capitalone.github.io · read 9 Oct 2026
- No hosted server requirement
- The FAQ says rubicon-ml does not require a hosted server for remote logging and supports local, S3, or in-memory data storage.capitalone.github.io · read 9 Oct 2026
- Prefect integration
- The Prefect integration adds rubicon-ml logging tasks to Prefect workflows to persist experimentation metadata.capitalone.github.io · read 9 Oct 2026
- Purpose
- Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way.capitalone.github.io · read 9 Oct 2026
- Scikit-learn integration
- RubiconPipeline can automatically log parameters and metrics when Scikit-learn pipelines are fit and scored.capitalone.github.io · read 9 Oct 2026
- Security guidance
- For EC2 S3 logging, the documentation says the IAM role needs s3:GetObject, s3:PutObject, and s3:DeleteObject permissions for the bucket.capitalone.github.io · read 9 Oct 2026
- Storage backends
- Documented backends include local filesystem, Amazon S3, in-memory storage, and Weights & Biases.capitalone.github.io · read 9 Oct 2026
