Summary
At my company some of the ML processes are still pretty immature. For example, if my teammate and I are testing two different modeling approaches, each approach ends up having multiple iterations like different techniques, hyperparameters, new datasets, etc. It quickly gets messy and it’s hard to keep track of which model run corresponds to what. We also end up with a lot of scattered Jupyter notebooks. To address this I’m trying to build a small internal tool. Since we only use XGBoost, the ...