Add https://youtu.be/zCupPHqSLTI to docs (#18743)

docs/en/guides/model-monitoring-and-maintenance.md

@@ -10,6 +10,17 @@ keywords: Computer Vision Models, AI Model Monitoring, Data Drift Detection, Ano
 
 If you are here, we can assume you've completed many [steps in your computer vision project](./steps-of-a-cv-project.md): from [gathering requirements](./defining-project-goals.md), [annotating data](./data-collection-and-annotation.md), and [training the model](./model-training-tips.md) to finally [deploying](./model-deployment-practices.md) it. Your application is now running in production, but your project doesn't end here. The most important part of a computer vision project is making sure your model continues to fulfill your [project's objectives](./defining-project-goals.md) over time, and that's where monitoring, maintaining, and documenting your computer vision model enters the picture.
 
+<p align="center">
+  <br>
+  <iframe loading="lazy" width="720" height="405" src="https://www.youtube.com/embed/zCupPHqSLTI"
+    title="YouTube video player" frameborder="0"
+    allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
+    allowfullscreen>
+  </iframe>
+  <br>
+  <strong>Watch:</strong> How to Maintain Computer Vision Models after Deployment | Data Drift Detection
+</p>
+
 In this guide, we'll take a closer look at how you can maintain your computer vision models after deployment. We'll explore how model monitoring can help you catch problems early on, how to keep your model accurate and up-to-date, and why documentation is important for troubleshooting.
 
 ## Model Monitoring is Key