Rails allows anyone to build a blog engine in 15 minutes; how would this translate to the Machine Learning development world? This post represents my vision of what a Ruby on Rails for Machine Learning should look like. I’ve been talking to many data practitioners these past few months, from Ploomber users to maintainers of other data tools. A recurrent topic has been the state of the Machine Learning tooling; discussions often revolve around the proper API for a Machine Learning framework.

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 files to track experiments without any extra infrastructure. What is an experiment tracker?

Notebooks are a magnificent tool to explore data, but such a powerful tool can become hard to manage quickly. Ironically, the ability to interact with our data rapidly (modify code cells, run, and repeat) is the exact reason why a notebook may become an obscure entanglement of variables that are hard to understand, even to the notebook’s author. But it doesn’t have to be that way. This post summarizes my learnings over the past few years on writing clean notebooks.