# 5.6. Run data drift and model quality checks in an Airflow pipeline \[OPTIONAL CODE PRACTICE]

{% embed url="<https://youtu.be/YHO7k3T_fZA?si=9ePxgZso8mA4CsFR>" %}

**Video 6**. [Run data drift and model quality checks in an Airflow pipeline \[OPTIONAL CODE PRACTICE\]](https://youtu.be/YHO7k3T_fZA?si=9ePxgZso8mA4CsFR), 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](https://github.com/evidentlyai/ml_observability_course/tree/main/module5/airflow_conditional_checks) to follow along.

**Outline:**\
[00:00](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=0s) Introduction\
[01:09](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=69s) Install Airflow\
[02:47](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=167s) Install dependencies\
[05:20](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=320s) Rebuild the container and access Airflow UI\
[07:06](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=426s) Start creating the DAG\
[10:00](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=600s) Specify DAG parameters\
[12:48](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=768s) Add functions and implement DAG\
[17:55](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=1075s) Implement load data function\
[19:21](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=1161s) Implement drift analysis function\
[21:32](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=1292s) Implement create report function\
[23:14](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=1394s) View DAG in Airflow\
[26:06](https://www.youtube.com/watch?v=YHO7k3T_fZA\&t=1566s) Execute a DAG and view the drift report


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