# 5.8. Log data drift test results to MLflow \[CODE PRACTICE]

{% embed url="<https://youtu.be/gluRb9TbWSE?si=2Bw77DLhy_AKS-gz>" %}

**Video 8**. [Log data drift test results to MLflow \[CODE PRACTICE\]](https://youtu.be/gluRb9TbWSE?si=2Bw77DLhy_AKS-gz), by Emeli Dral

In this video, we show how to log the results of data drift tests implemented with Evidently to Mlflow and view the results in the MLflow interface.

**Want to go straight to code?** Here is the [code example](https://github.com/evidentlyai/ml_observability_course/tree/main/module5/mlflow_logging) to follow along.

**Outline:**\
[00:00](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=0s) Introduction\
[00:50](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=50s) Start creating an MLflow script\
[01:48](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=108s) Set up the database and preview the MLflow UI\
[03:10](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=190s) Writing the Python script: overview and imports\
[04:24](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=264s) Writing the Python script: set experiments\
[08:03](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=483s) Writing the Python script: log parameters and reports\
[10:15](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=615s) Run the script and view the results\
[13:30](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=810s) Run a new experiment to log HTML reports\
[16:27](https://www.youtube.com/watch?v=gluRb9TbWSE\&t=987s) Next module focus

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