There’s always a need for more tidymodels examples on the Internet. Here’s a simple machine learning model using the recent coffee Tidy Tuesday data set. The plot above gives the approach: I’ll define some preprocessing and a model, optimise some hyperparameters, and fit and evaluate the result. And I’ll piece all of the components together using targets, an experimental alternative to the drake package that I love so much.

I’m obsessed with how to structure a data science project. The time I spend worrying about project structure would be better spent on actually writing code. Here’s my preferred R workflow, and a few notes on Python as well.

Suppose I want a function that runs some setup code before it runs the first time. Maybe I’m using dplyr but I haven’t properly declared all of my dplyr calls in my function, so I want to run library(dplyr) before the actual function is run. Or maybe I want to install a package if it isn’t already installed, or restore a renv file, or any other setup process. I only want this special code to run the first time my function is called. After that, the function that runs should be…

MLflow is a platform for the “machine learning cycle”. It’s a suite of tools for managing models, with tracking of hyperparameters and metrics, a registry of models, and options for serving. It’s this last bit that I’m going to focus on today.

Machine learning models get stuck at the deployment stage all the time. This stuff is hard.