Intro to MLOps: Experiment Tracking for Machine Learning

Why it matters and three different ways you can log and organize your ML experiments with pen and paper, spreadsheets, and modern experiment tracking tools.

Image created by the author using DALLE2 with the prompt “oil painting of a mad scientist cat that is conducting a chemistry experiment in a lab, highly detailed”

This article was originally published on the Weights & Biases’ blog “Fully Connected” on December 16th, 2022.

Imagine you are trying to develop a recipe for the best chocolate chip cookies. After the first try, you might increase the amount of flour. One time, you might add more chocolate chips. Another time you might try it with some walnuts. In the end, you might have tried a dozen recipes, but which was the best?

In the end, you might have tried a dozen recipes, but which was the best?

I’m sure you agree that taking notes during this process would be a good idea. You should probably write down the ingredients of each recipe and how the resulting cookies tasted.

This approach also applies to developing Machine Learning (ML) models. Developing an ML model takes many experiments because small changes in the input — like the ingredients — can greatly impact the results — like the taste of the cookies. Thus, tracking your experiments is a good…