# 3.5. Monitoring text data \[CODE PRACTICE]

{% embed url="<https://www.youtube.com/watch?v=RIultWCjYXo&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF&index=19>" %}

**Video 5**. [Monitoring text data \[CODE PRACTICE\]](https://www.youtube.com/watch?v=RIultWCjYXo\&list=PL9omX6impEuOpTezeRF-M04BW3VfnPBRF\&index=19), by Emeli Dral

In this video, we walk you through the code example of evaluations for unstructured data 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/module3/unstructured_data_code_practice.ipynb) to follow along.

**Links to docs:**

* [Text overview](https://docs.evidentlyai.com/presets/text-overview)
* [Embeddings](https://docs.evidentlyai.com/user-guide/customization/embeddings-drift-parameters)

**Outline:**\
[00:00](https://youtu.be/RIultWCjYXo?si=5s0_-fMduGKorqci) Import libraries and datasets\
[02:26](https://youtu.be/RIultWCjYXo?si=Vyrnq26avImqSUB6\&t=146) Prepare a multimodal dataset with raw text\
[08:15](https://youtu.be/RIultWCjYXo?si=hKrfvOBPZ3kFeisC\&t=495) Text data overview report\
[11:18](https://youtu.be/RIultWCjYXo?si=nws_RxLC2YsoiD1C\&t=678) Model-based text data drift detection\
[13:49](https://youtu.be/RIultWCjYXo?si=WlLgpbHHt2Bi-UIH\&t=829) Adding custom text descriptors to report\
[17:50](https://youtu.be/RIultWCjYXo?si=9KWXxjYqW4eaE97n\&t=1070) Custom report with descriptor drift detection\
[23:55](https://youtu.be/RIultWCjYXo?si=9lNBhLuipZrDK8zi\&t=1435) Embedding drift detection

That’s it! We covered three use cases for evaluating unstructured data – multimodal data with raw text, detecting drift in text data with descriptors, and embedding drift detection – and learned to derive actionable metrics.


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