Evidently integrations

Overview of the available Evidently integrations.

You are looking at the old Evidently documentation: this API is available with versions 0.6.7 or lower. Check the newer docs version here.

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