A code example walkthrough of automating data and model quality checks implemented with the Evidently Python library using Airflow.
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Video 6. Run data drift and model quality checks in an Airflow pipeline [OPTIONAL CODE PRACTICE], by Emeli Dral
In this video, we show how to automate the data or model quality checks implemented with the Evidently Python library using Airflow.
Want to go straight to code? Here is the code example to follow along.
Outline: 00:00 Introduction 01:09 Install Airflow 02:47 Install dependencies 05:20 Rebuild the container and access Airflow UI 07:06 Start creating the DAG 10:00 Specify DAG parameters 12:48 Add functions and implement DAG 17:55 Implement load data function 19:21 Implement drift analysis function 21:32 Implement create report function 23:14 View DAG in Airflow 26:06 Execute a DAG and view the drift report
