| description |
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Overview of the available Evidently integrations. |
Evidently is a Python library, and can be easily integrated with other tools to fit into the existing workflows.
Below are a few specific examples of how to integrate Evidently with other tools in the ML lifecycle. You can adapt them for other workflow management, visualization, tracking and other tools.
| Tool | Description | Guide or example |
|---|---|---|
| Notebook environments (Jupyter, Colab, etc.) | Render visual Evidently Reports and Test Suites. | Docs Code examples |
| Streamlit | Create a web app with Evidently Reports. | Tutorial Code example |
| MLflow | Log metrics calculated by Evidently to MLflow. | Docs Code example |
| DVCLive | Log metrics calculated by Evidently to DVC. | Docs Code example |
| Airflow | Run data and ML model checks as part of an Airflow DAG. | Docs Code example |
| Metaflow | Run data and ML model checks as part of a Metaflow Flow. | Docs |
| FastAPI + PostgreSQL | Generate on-demand Reports for models deployed with FastAPI. | Tutorial Code example |
| Grafana + PostgreSQL + Prefect | Run ML monitoring jobs with Prefect and visualize metrics in Grafana. | Tutorial Code example |
| AWS SES | Send email alerts with attached Evidently Reports (Community contribution). | Tutorial Code example |
| Grafana | Real-time ML monitoring with Grafana. (Old API, not currently supported). | Code example |