Evidently integrations

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.
​Code example
Blog tutorial​
MLflow
Log metrics calculated by Evidently to MLflow.
​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​
Grafana, PostgreSQL, and 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).
​Docs
Code example​

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.	Code example Blog tutorial
MLflow	Log metrics calculated by Evidently to MLflow.	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
Grafana, PostgreSQL, and 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).	Docs Code example

Integrations

Notebook environments

Last modified 13d ago