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.

!pip install evidently

Import the components to work with the dataset and send the metrics.

import pandas as pd
from sklearn import datasets

from evidently.ui.workspace.cloud import CloudWorkspace
from evidently.report import Report
from evidently.metric_preset import DataQualityPreset

5. Create a new Project

Connect to Evidently Cloud using your access token.

ws = CloudWorkspace(token="YOUR_TOKEN_HERE", url="https://app.evidently.cloud")

Create a new Project inside your Team. Pass the team_id.

project = ws.create_project("My test project", team_id="YOUR_TEAM_ID")
project.description = "My project description"

6. Collect metrics

Import the demo "adult" dataset as a pandas DataFrame.

adult_data = datasets.fetch_openml(name="adult", version=2, as_frame="auto")
adult = adult_data.frame

Run a Data Quality Report and upload it to the Project.

data_report = Report(
data_report.run(reference_data=None, current_data=adult)
ws.add_report(project.id, data_report)

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 Monitoring

Working with LLMs? See a Quickstart.

Evidently LLM Quickstart

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