Track Your ML Experiments

A guide to Neptune.ai for tracking your experiments in Python

Photo by Alex Kondratiev on Unsplash

Every data scientist is familiar with experimentation.

You know the drill. You get a dataset, load it into a Jupyter notebook, explore it, preprocess the data, fit a baseline model or two, and then train an initial final model, such as XGBoost. The first time around, maybe you don’t tune the…