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Rubicon-ML

capitalone.github.io

Free plan WebLinuxAPISelf-hosted
Visit capitalone.github.io
Spec score 8.3 #7 of 39 in ML Experiment Tracking Software

What it is

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.

Spec sheet

Purpose
Rubicon-ML captures and stores model training and execution information, including parameters and outcomes, in a repeatable and searchable way.Read 9 Oct 2026 · capitalone.github.io
Intended users
The package metadata identifies developers and science or research users as its intended audience.Read 9 Oct 2026 · github.com
Integrations
The documentation includes integrations for Git, Prefect, and Scikit-learn.Read 9 Oct 2026 · capitalone.github.io
Support
The project README directs users to open a GitHub issue to report a bug or make a suggestion.Read 9 Oct 2026 · github.com
License
The project is licensed under Apache License 2.0.Read 9 Oct 2026 · github.com
Headquarters
McLean, Virginia, United StatesRead 28 Sep 2026 · capitalone.github.io
Founded
1994Read 28 Sep 2026 · capitalone.github.io
Auditability
Its Git integration associates model inputs and outputs with the model code that produced them to support auditability and reproducibility.Read 9 Oct 2026 · capitalone.github.io
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.Read 9 Oct 2026 · capitalone.github.io
Concurrent logging
Rubicon-ML supports logging multiple experiments in parallel and asynchronous communication with S3.Read 9 Oct 2026 · capitalone.github.io
Dashboard deployment
The dashboard is a Dash app that can be dockerized like other Dash apps.Read 9 Oct 2026 · capitalone.github.io
Experiment review
The dashboard lets users explore, filter, visualize, and share logged work with reviewers and stakeholders.Read 9 Oct 2026 · capitalone.github.io
Git audit trail
Its Git integration can record repository origin, branch names, and commit hashes alongside projects and experiments.Read 9 Oct 2026 · capitalone.github.io
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.Read 9 Oct 2026 · capitalone.github.io
Installation
The Python package can be installed using pip or conda-forge.Read 9 Oct 2026 · capitalone.github.io
Logging
The logging component organizes, stores, and retrieves model inputs and outputs using pluggable storage backends powered by fsspec.Read 9 Oct 2026 · capitalone.github.io
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.Read 9 Oct 2026 · capitalone.github.io
Prefect integration
The Prefect integration adds rubicon-ml logging tasks to Prefect workflows to persist experimentation metadata.Read 9 Oct 2026 · capitalone.github.io
Scikit-learn integration
RubiconPipeline can automatically log parameters and metrics when Scikit-learn pipelines are fit and scored.Read 9 Oct 2026 · capitalone.github.io
Security guidance
For EC2 S3 logging, the documentation says the IAM role needs s3:GetObject, s3:PutObject, and s3:DeleteObject permissions for the bucket.Read 9 Oct 2026 · capitalone.github.io
Storage backends
Documented backends include local filesystem, Amazon S3, in-memory storage, and Weights & Biases.Read 9 Oct 2026 · capitalone.github.io
Storage options
Documented backends include local filesystem, Amazon S3, in-memory storage, and Weights & Biases.Read 9 Oct 2026 · capitalone.github.io
Visualization
The dashboard and widgets let users explore and compare logged model metadata.Read 9 Oct 2026 · capitalone.github.io
Visualization dependencies
The visualization tools are an optional pip extra, while conda installs the optional extras by default.Read 9 Oct 2026 · capitalone.github.io
W&B limitation
The W&B backend is documented as experimental and does not support project-level artifacts or dataframes, or get_projects() listing.Read 9 Oct 2026 · capitalone.github.io
Weights & Biases limitation
The W&B backend is experimental and does not support project-level artifacts or dataframes or listing all projects with get_projects().Read 9 Oct 2026 · capitalone.github.io

Plans and prices

rubicon-ml Free Open-source Python package · install with pip or conda Read 9 Oct 2026 · capitalone.github.io

Every Rubicon-ML plan and price →

Strong points and limits

Strong points

  • Open-source package costs nothing
  • Git records repository and commit context
  • Parallel and asynchronous experiment logging
  • Pluggable local, S3, memory, and W&B storage
  • Prefect and Scikit-learn integrations

Limits

  • Weights & Biases backend is experimental
  • W&B lacks project artifacts and dataframes
  • Visualization tools require an optional pip extra

Who it suits

It is aimed at developers and science or research users who need repeatable experiment records. Teams can review and share logged metadata through its dashboard.

Verdict

Rubicon-ML focuses on searchable, auditable experiment metadata without requiring a hosted server. Its integrations and storage choices support varied workflows, with documented limitations for the W&B backend.

Where Specifiction places it

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