About
Evidently is a robust open-source framework designed for evaluating, testing, and monitoring machine learning (ML) and large language model (LLM) systems. As a Python library, it caters to both experimental setups and production environments, effectively handling tabular and text data. With Evidently, users can leverage over 100 built-in metrics for various tasks, including data drift detection and generative evaluations. The tool's modular architecture allows for one-off evaluations or hosting a comprehensive monitoring service, making it versatile for different user needs.
Highlights
- Comprehensive Reporting: Evidently generates detailed reports that summarize various data quality evaluations, which can be customized or started with built-in presets. These reports are perfect for exploratory data analysis and debugging.
- Test Suites: Turn reports into test suites by adding specific pass/fail conditions, ideal for regression testing, CI/CD checks, or data validation.
- Monitoring Dashboard: The monitoring service visualizes metrics and test results over time. Users have the option to self-host the open-source version or use Evidently Cloud, which offers additional features like dataset management and alerting.
- Easy Integration: Evidently’s open architecture facilitates easy export of data and integration with existing tools, enhancing your workflow efficiency.
- User-Friendly Installation: Installation is straightforward with options from PyPI or conda.
Overall, Evidently stands out as a strategically designed framework that aids users in effectively monitoring and evaluating their machine learning systems, ensuring robust quality control and insightful analysis.