How to track experiment metrics across different machine learning models?

Hey DVC crew! :heart_eyes:

I have been rocking DVC for my machine learning experiments, but I’m stuck on the best way to track metrics across a bunch of different models. Here’s the situation: I have several models trained with various algorithms and hyperparameters, and I want to see how their performance (accuracy, precision, recall, etc.) stacks up over time.

Wondering what the DVC experts recommend here. Should I create separate metrics files for each model version, versioned with DVC of course, or is there a more streamlined way to manage this within DVC itself, like aggregating and comparing the metrics directly?
I also check this : But I have not found any solution.

Any tips or tricks you can share would be awesome!

Thanks in advance!

Hey @nancy79 ! Do you use DVCLive to collect metrics? I would recommend checking the DVCLive + VSCode combination for your task! You may save all metrics in a single file or separately and visualize them together in VSCode UI.

Docs references