# 6.3. ML model monitoring dashboard with Evidently. Online architecture \[CODE PRACTICE]

{% embed url="<https://youtu.be/2hTRXEOJF8k?si=CCVRwxiWWyZGmZF7>" %}

**Video 3**. [ML model monitoring dashboard with Evidently. Online architecture \[CODE PRACTICE\]](https://youtu.be/2hTRXEOJF8k?si=CCVRwxiWWyZGmZF7), by Emeli Dral

In this video, we create a live ML monitoring dashboard for an ML model deployed as a service. We imitate sending the live data directly from the machine learning service to the ML monitoring service and update the dashboard in near real-time.

**Want to go straight to code?** Here is the [code example](https://github.com/evidentlyai/ml_observability_course/blob/main/module6/online_monitoring_dashboard.py) to follow along.

**Outline:**\
[00:00](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=0s) Introduction\
[00:30](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=30s) Script overview and imports\
[01:59](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=119s) Define Collector, Workspace, and Project variables\
[03:31](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=211s) Load data and create mini-batches to simulate production usage\
[04:39](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=279s) Implement the function to generate Test Suites\
[06:38](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=398s) Create the Workspace, Project and add Dashboard panels\
[09:25](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=565s) Set up and configure the Collector service\
[13:00](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=780s) Simulate sending data to the Collector\
[15:48](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=948s) Implement the main function, run and debug the script\
[18:32](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=1112s) Run the Collector and view the online Dashboard updates\
[20:46](https://www.youtube.com/watch?v=2hTRXEOJF8k\&t=1246s) Recap and next steps