Hello, everyone and welcome to another update for envelope circulation engineering, I've, been working on machine learning, expert tracking for a bit and I'm excited that today today to show a bit of a demo, it's a very simple one, uh but it I hope that it displays a little bit. What's the user experience that we want to have here? uh What are we aiming for so I have this very simple training uh script.

This was actually taken from the mlflow uh sample uh repository, so it just trains, a very simple experiment, machine learning, expert uh model and then uses demoflow client to save parameters and metrics and what it does uh when you are working with it. This is what it looks like uh so I'm gonna use, ml flow itself and I export and then I do python train dot by, and that should give us over here if I, just uh refresh, that will show my new experiment created over here and everything. So this is the ammo flow experience.

uh I want to do something similar for gitlab that, in a way that adds the least uh friction for our users.

uh So I could, whenever you create an experiment, a project you're gonna, have now a new uh option for the machine learning experience experiments in this case there is nothing to show, but for you to start logging, your models, uh intricate lab now or your experiments, you you just have to change to uh environment variables, so you need to, of course, change the your eye that you're pointing to, and you have to set the tracking token and by just doing this you'll run the same script that we had before nothing changes and it creates the experiment and now I can come over here.

Wait a bit refresh and I can see my experimenter over here. If I click it, it will start showing the parameters and the metrics that I've chipped. This is a very simple. It's still a very simple uh version. We want to improve a lot. This front end, add search, add comparison, make it more uh usable, but this is the experience we're aiming for zero, almost zero configuration by the user, no changes necessary to their code for it to work, and it just simply works with gitlab, so it uses the mlflow client.

But now gitlab manages your experiments.

um Yeah, that was what I had to show today: uh I hope that was fun for you. Thank you. Bye.