Features
- Model Quality: Evaluate the quality of machine learning models, identifying areas of failure beyond aggregate performance metrics.
- Data Drift: Conduct statistical tests to compare input feature distributions and visually explore data drift in machine learning models.
- Target Drift: Understand changes over time in model predictions and target behavior, addressing potential issues proactively.
- Data Quality: Provide a snapshot of data health, allowing users to drill down and explore feature behavior and statistical properties.
Use Cases:
- Dashboard: Generate interactive reports in the notebook or export them as HTML files for visual evaluation, debugging, and sharing with the team.
- Pipeline: Run data and model checks as part of the pipeline, integrating with MLflow or Airflow to schedule tests and log results efficiently.
- Monitoring Service: Collect model quality metrics from deployed ML services via seamless integration with Prometheus and Grafana for robust monitoring.
Evidently AI stands out as a promising open-source tool that simplifies model monitoring and debugging. Offering features such as model quality evaluation, data drift detection, target drift understanding, and data health assessment, it can be utilized as a dashboard, pipeline component, or monitoring service. With positive feedback from its community, a user-friendly interface, and easy implementation, Evidently AI proves to be a valuable tool for anyone involved in production-level machine learning.
