An experiment tracker enables you to reproduce any model from the past. It doesn’t do this alone — to have full reproducibility you also need data version control and source control — but an experiment tracker is the only tool that combines all of the relevant information about a model.
When a model’s performance changes, experiment trackers allow you to go back and understand why, and this in turn means you can make the right decisions to improve your model in the future.
Or, if you have a particular experiment that you’d like to share with a colleague, in order to get their input or a review, an experiment tracker makes it easy for your colleague to see not only the end result but exactly how you got there.
As a tool both for ensuring reproducibility and enabling collaboration, we think experiment tracking is a key piece of your MLOps infrastructure.