# 6.4. ML monitoring with Evidently and Grafana \[OPTIONAL CODE PRACTICE]

{% embed url="<https://youtu.be/S4zFqbLhAp8?si=BEfLZteDmj94XPpD>" %}

**Video 4**. [ML monitoring with Evidently and Grafana \[OPTIONAL CODE PRACTICE\]](https://youtu.be/S4zFqbLhAp8?si=BEfLZteDmj94XPpD), by Emeli Dral

In this video, we show how to add ML monitoring metrics to the Grafana dashboard using Evidently as an evaluation layer and storing the metrics in a Postgres database.

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

**Outline:**\
[00:00](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=0s) Introduction\
[00:25](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=25s) Install Grafana and set up the Postgres database\
[03:18](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=198s) Script overview, imports, and logging\
[04:50](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=290s) Create a table statement\
[06:30](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=390s) Load data and simulate production usage\
[07:30](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=450s) Connect to a database and create a table\
[10:32](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=632s) Calculate metrics and insert them into Postgres\
[16:00](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=960s) Write a function to compute metrics in batches\
[18:58](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=1138s) Run services, execute and debug the script\
[23:21](https://www.youtube.com/watch?v=S4zFqbLhAp8\&t=1401s) Create dashboards in Grafana