MAIF / eurybia
⚓ Eurybia monitors model drift over time and securizes model deployment with data validation
Why this job: works in the areas we cover, but does not say which job it does
What it does
- Allow Data Scientists to quickly explore drift thanks to dynamic reports to easily navigate between drift detection and datasets features.
- Monitoring drift using a scheduler (like Airflow)
- Evaluate level of data drift
- Facilitate collaboration between data analysts and data scientists, and easily share and discuss results with non-Data users
Get started
No single install command. Read the steps in the README (opens in new tab)
- Maintenance
- Active
- Last commit 17 days ago
- Stars
- 224
- On GitHub
- Licence
- Apache-2.0
- Allows business use
- Downloads for
- None listed
- No release files
- Latest version
- v1.4.2
- Released 17 days ago
- Built with
- Jupyter Notebook
- 90% of the code
Is it looked after?
- Contributors
- Not stated
- Last 90 days
- Issues
- 65% closed
- 13 still open
- Pull requests
- 55 merged
- 2 waiting
- Releases
- 13
- 3 in the last year
- Contributing guide (opens in new tab)
- No security policy
- No code of conduct
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More: languages, topics and project record
Languages
- Jupyter Notebook 90%
- Python 6%
- CSS 4%
- Makefile <1%
Topics
- python
- data-validation
- drift
- html-report
- machine-learning
- data-drift
- domain-classifier
- drift-detection
- model-monitoring
- production-machine-learning
- datadrift-classifier
- model-drift
Project record
- JobOther AI tools
- StatusListed
- Commits152
- Forks27
- Watching4
- Stars in 28 days0
- CreatedMay 2, 2022
- First seen by usNov 7, 2026
- CheckedNov 8, 2026