2.5. Data quality in ML [CODE PRACTICE]

A code example walkthrough of data quality evaluation using Evidently Reports and Test Suites.

Video 5. Data quality in ML [CODE PRACTICE], by Emeli Dral

In this video, we walk you through the code example of data quality evaluation using Evidently Reports and Test Suites.

Want to go straight to code? Here is the example notebook to follow along.

Here is a quick refresher on the Evidently components we will use:

• Reports compute and visualize 100+ metrics in data quality, drift, and model performance. You can use in-built report presets to make visuals appear with just a couple of lines of code.

• Test Suites perform structured data and ML model quality checks. They verify conditions and show which of them pass or fail. You can start with default test conditions or design your testing framework.

Outline: 00:00 Create a working environment and import libraries 01:30 Prepare reference and current dataset 05:20 Run data quality Test Suite and visualize the results 09:30 Customize the Test Suite by specifying individual tests and test conditions 13:20 Build and customize data quality Report

That’s it! We evaluated data quality using Evidently Reports and Test Suites and demonstrated how to add custom metrics, tests, and test conditions to the analysis.