Notebook meta-analysis: Jupyter as a zero-infrastructure alternative to experiment trackers

Existing experiment trackers come with a high setup cost. To get one working, you usually have to spin up a database and run a web application. After trying multiple options, I thought that using Jupyter notebooks could be an excellent choice to store experiment results and retrieve them for comparison. This post explains how I use .ipynb Machine Learning is a highly iterative process: you don't know in advance what combination of model, features, and hyperparameters will work best, so you need to make slight tweaks and evaluate performance.