Deepchecks tests and validates machine learning models and data, covering workflows from research through production.
Eurybia
Eurybia tracks model drift and validates data during machine learning model deployment.

About Eurybia
Eurybia is an open-source Python library from the MAIF GitHub project for monitoring model drift over time. It helps you check data before a model goes into production. You pass a baseline or training dataset and a current or production dataset as pandas DataFrames, and you can add a deployed model and encoder. The usual workflow declares a SmartDrift object, compiles it, and generates an HTML report. It installs with pip and is intended for Python 3.9 to 3.12.
Pricing is not clearly stated.
Key features
- Feature importance, scatter plot and dataset distribution views
- AUC evolution and model performance evolution views
- Predicted value distribution comparison between baseline and newest data
- HTML reports that can be shared with non-data users
- Installs with pip
- Optional deployed model and encoder for richer report content
- Model and encoder results need datasets the model can use
- Intended for Python versions 3.9 to 3.12
Eurybia at a glance
- Website
- github.com/MAIF/eurybia
- Type
- Open-source project
- Pricing
- Not confirmed. Check the site
- Details verified
- Oct 22, 2026 · Official source
- Open source
- Yes · Source code
- Language
- English
- Last checked
- Oct 5, 2026 · Online when checked
- Listed
- Oct 5, 2026
Eurybia alternatives
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Evidently evaluates, tests, and monitors machine learning systems and data pipelines.
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MMDeploy provides a model deployment framework from the OpenMMLab project.
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