Introduction
Evidently is an open-source ML and LLM observability framework for evaluating, testing, and monitoring models from experiment to production. It provides Reports, Test Suites, and a Monitoring UI, and includes over 100 built-in metrics for data and model quality.
Key Features
- Rich reports and test suites with presets and export options (JSON/HTML).
- Offline and live monitoring with historical trend visualization.
- Extensible metrics and LLM-as-a-judge integrations for generative evaluations.
Use Cases
- Experiment-level model evaluation and comparisons.
- CI/CD regression testing and data drift detection.
- Production model monitoring and alerting dashboards.
Technical Highlights
- Supports presets like DataDrift and TextEvals and 100+ built-in metrics.
- Offers a self-hosted Monitoring UI and Evidently Cloud managed service and demo.
- Integrates with common tooling (Pandas, Hugging Face) and supports varied deployment patterns.