We focussed on simplifying the experimentation experience that a data scientist / engineer typically go through.

For instance, if you want to find the best LLM with the best configuration for your dataset, then ideally, we would like to run an ablation study (think grid search over learning rate, number of epochs, etc.). It would be challenging to show this progress over an UI.

The ideal user of the toolkit would set all the experimentation details in a config file, and then run it via the terminal -- come back to it after a day or so, depending on how big the search space is.