Confident AI
DeepEval-powered LLM evaluation platform to test, benchmark, and safeguard apps
Evidently
Evaluate, test, and monitor ML & LLM systems effortlessly
A Python library that provides 100+ built‑in metrics, customizable evaluations, and a monitoring UI for both tabular and generative AI models, supporting offline analysis and live production tracking.
Overview
Overview
Evidently is a Python library designed for evaluating, testing, and monitoring machine‑learning and large‑language‑model pipelines. It ships with more than a hundred ready‑to‑use metrics covering data quality, drift detection, classification, regression, ranking, and LLM‑specific judges. Users can generate interactive Reports, turn them into Test Suites with pass/fail thresholds, and export results as JSON, HTML, or Python dictionaries.
Deployment
The framework works locally via a lightweight UI that can be self‑hosted, or through Evidently Cloud for a managed experience with alerts and dataset management. Installation is a single pip install evidently (or Conda) command, after which reports and monitoring dashboards can be launched from a notebook or a terminal. Custom metrics are added through a simple Python interface, making the library adaptable to any domain‑specific evaluation need.
Highlights
100+ built‑in metrics for tabular and generative tasks
Modular Reports that can be converted into pass/fail Test Suites
Self‑hosted monitoring UI with optional managed Cloud service
Python API for creating custom metrics and exporting data
Pros
- Extensive metric library reduces need for third‑party tools
- Supports both offline evaluation and live production monitoring
- Flexible architecture allows easy integration with existing pipelines
- Open source with community‑driven extensions
Considerations
- Requires a Python environment; not native to other languages
- Self‑hosting the UI adds operational overhead
- Custom metric development needs Python coding
- Learning curve for advanced presets and dashboard configuration
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Fit guide
Great for
- Data scientists building reproducible ML evaluation pipelines
- ML engineers who need regression testing in CI/CD workflows
- Teams deploying LLM applications that require quality judges
- Organizations wanting real‑time performance dashboards
Not ideal when
- Projects that rely on non‑Python stacks
- Very small prototypes without monitoring needs
- Teams without Python expertise or resources to self‑host UI
- Environments where external cloud services are prohibited
How teams use it
Detect data drift between training and production
Early alerts when feature distributions shift, preventing model degradation
Automate LLM response quality checks in CI
Pass/fail test suites ensure new releases meet predefined quality thresholds
Generate interactive reports for model debugging
Visual summaries of metrics help pinpoint performance bottlenecks
Deploy a live monitoring dashboard for production models
Continuous visibility and alerting on key performance indicators
Tech snapshot
Jupyter Notebook74%
Python25%
TypeScript2%
Makefile1%
HTML1%
JavaScript1%
Frequently asked questions
How do I install Evidently?
Run `pip install evidently` or `conda install -c conda-forge evidently`.
Can I run a report without the UI?
Yes, reports can be executed in Python and exported as JSON, HTML, or dictionaries.
What is the difference between the open‑source UI and Evidently Cloud?
The OSS UI is self‑hosted; Cloud provides managed hosting, alerting, and additional admin features.
How do I add a custom metric?
Implement a Python class following Evidently’s metric interface and include it in a Report.
Can I set pass/fail thresholds for metrics?
Yes, Test Suites let you define `gt` (greater than) or `lt` (less than) conditions for any metric.
Project at a glance
Active- Stars
- 7,019
- Watchers
- 7,019
- Forks
- 771
LicenseApache-2.0
Repo age5 years old
Last commitlast week
Primary languageJupyter Notebook
Last synced yesterday