Hello, everyone and welcome to another update for incubation engineer mlaps at gitlab. My name is eduardo and today is august 8th uh august 12th, and this is the first time I'm not going to talk about jupiter notebooks, because now we're moving on with machine learning.

Experiment tracking uh we've been talking about this for a while, under the guise of let's create a let's deploy more flow along gitlab, but I've been we've been chatting around the technical side and how to learn this and we've decided to implement machine learning, experiment tracking within gitlab itself, reasons being it's better to integrate across the ecosystem.

The unique value proposition here is that this is not just a tool that acts on its own. This is supposed to be a tool that integrates across the entire gitlab so, for example, machine learning the experiment tracking can integrate to merge requests. When you create a merge request that contains a model, it already creates uh the experiment tracking. For that specs. From tracking I mean you train a lot of different models which we'll call candidates and with different parameters, and then you can compare the results for each of them.

So we already started working on this. The first, mr, is already uh being reviewed, and we expect to have the mvp by 15.5, the 15.5. There we defined the mvp as having users being able to log the experiments, log, the candidates, log, the parameters and metrics into gitlab, but furthermore,.

If you have, if you already use mflow the example that we, the the example that there is out there for for getting started, should work with gitlab by just replacing your eye. That is the goal of the mvp and we hope to achieve this by 15.5.

So we will be working along this in the next few weeks and I'll give updates as they come. But we're really excited about starting this new project see you next time.