# 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


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://learn.evidentlyai.com/ml-observability-course/module-6-deploying-an-ml-monitoring-dashboard/online-ml-monitoring-dashboard-code-practice.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.