Neptune is aesthetic. Therefore we could simply use the visualization it was generating in our reports.
Senior Data Scientist at deepsense.ai
We trained more than 120.000 models in total, for more than 7000 subproblems identified by various combinations of features. Due to Neptune, we were able to filter experiments for given subproblems and compare them to find the best one. Also, we stored a lot of metadata, visualizations of hyperparameters’ tuning, predictions, pickled models, etc. In short, we were saving everything we needed in Neptune.