# 2.8. Data and prediction drift in ML \[CODE PRACTICE]

{% embed url="<https://www.youtube.com/watch?v=oO1K4CaWxt0&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=14>" %}

**Video 8**. [Data and prediction drift in ML \[CODE PRACTICE\]](https://www.youtube.com/watch?v=oO1K4CaWxt0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=14), by Emeli Dral

In this video, we walk you through the code example of detecting data drift and creating a custom method for drift detection using the open-source [Evidently](https://github.com/evidentlyai/evidently) Python library.

**Want to go straight to code?** Here is the [example notebook](https://github.com/evidentlyai/ml_observability_course/blob/main/module2/data_drift_deep_dive.ipynb) to follow along.

**Outline**:\
[00:00](https://www.youtube.com/watch?v=oO1K4CaWxt0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=14\&t=0s) Create a working environment and import libraries\
[01:33](https://www.youtube.com/watch?v=oO1K4CaWxt0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=14\&t=93s) Overview of the data drift options\
[04:25](https://www.youtube.com/watch?v=oO1K4CaWxt0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=14\&t=265s) Evaluating share of drifted features\
[06:40](https://www.youtube.com/watch?v=oO1K4CaWxt0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=14\&t=400s) Detecting column drift\
[11:47](https://www.youtube.com/watch?v=oO1K4CaWxt0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=14\&t=707s) Set different drift detection method per feature type\
[12:57](https://www.youtube.com/watch?v=oO1K4CaWxt0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=14\&t=777s) Set individual different drift detection methods per feature\
[15:34](https://www.youtube.com/watch?v=oO1K4CaWxt0\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=14\&t=934s) Custom drift detection method

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