Evidently Cloud Quickstart
ML Monitoring “Hello world.” From data to dashboard in a couple of minutes.
1. Create an account
If not already, sign up for an Evidently Cloud account. Create your Organization.
2. Create a team
Click on the Teams icon on the left menu. Create a Team - for example, "Personal". Copy and save the team ID. (Team page).
3. Get an access token
Click the Key icon in the left menu to go. Generate and save the token. (Token page).
4. Install the Python library
Install the Evidently Python library. You can run this example in Colab or another Python environment.
Import the components to work with the dataset and send the metrics.
5. Create a new Project
Connect to Evidently Cloud using your access token.
Create a new Project inside your Team. Pass the team_id
.
6. Collect metrics
Import the demo "adult" dataset as a pandas DataFrame.
Run a Data Quality Report and upload it to the Project.
We call each such evaluation a snapshot
.
7. View the Report
Visit Evidently Cloud, open your Project, and navigate to the "Report" tab to see the data stats.
8. Add a monitoring panel
Go to the "Dashboard" tab and enter the "Edit" mode. Add a new tab, and select the "Data quality" template.
You'll see a set of panels with a single data point. As you send more snapshots, you can track trends and set up alerts. You can choose from 100+ metrics and tests on data quality, data drift, ML quality (regression, classification, ranking, recsys), LLM quality and text data, and add your own metrics.
Want to see more?
Check out a more in-depth tutorial to learn the key workflows:
Tutorial - Data & ML MonitoringWorking with LLMs? See a Quickstart.
Evidently LLM QuickstartLast updated