
Simple Example
You must first connect to Evidently Cloud and create a Project.
Workflow
The complete workflow looks as the following.1
Configure the metrics
Define an Evidently Report with optional Test conditions to define the evals.
2
Run the evals
You must independently execute Reports on a chosen cadence. Consider tools like Airflow. You can send Reports from different steps in your pipeline. For example:
- first, send data quality, data drift and prediction drift checks
- after you get the delayed labels, send a ML quality checks results.

3
Upload to the platform
Choose to store raw inferences or only upload the metric summaries. How to upload / delete results.
4
Configure the Dashboard
Set up a Dashboard to track results over time: using pre-built Tabs or configure your own choice of monitoring Panels. Check the Dashboard guide.
5
Configure alerts
Set up alerts on Metric values or Test failures. Check the section on Alerts.
Running Tests vs Reports. Structuring your evaluations as Tests - as opposed to monitoring lots of metrics at once - can help reduce alert fatigue and simplify configuration when evaluating multiple conditions at once. For example, you can quickly verify that all columns in the input data are within a defined min-max range.