deepchecks/deepchecks
Deepchecks is an open-source platform providing continuous validation for AI and ML models and data from research to production, focusing on testing, CI, and monitoring.
Awesome Infra for AI › Model & Data Drift Monitoring
MAIF/eurybia
Eurybia is a Python library designed to address the challenges of industrializing and maintaining machine learning models over time. Its primary functions include detecting data drift and model drift, as well as validating data before deploying models into production. By providing tools for continuous monitoring, Eurybia contributes to better model auditing, AI governance, and overall model quality. The library generates interactive HTML reports that visualize key aspects of drift, such as feature importance, scatter plots, dataset distribution comparisons, predicted value analysis, and the performance evolution of data drift classifiers. These reports facilitate quick exploration of drift, aid in evaluating the level of data drift, and improve collaboration among data professionals by making results easily shareable and understandable to non-technical users. Eurybia integrates seamlessly into ML lifecycles, focusing on the post-deployment monitoring phase. It supports scheduling for continuous monitoring and provides clear insights into how data changes affect model performance, promoting proactive maintenance of AI systems.
https://github.com/MAIF/eurybia
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