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. As usual, I don’t care too much about the model itself.| mdneuzerling.com
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. I haven’t been able to find much discussion or documentation about MLflow’s support for R. There’s the RStudio MLflow example, but I wanted to see if I could use MLflow to serve something more complex.| mdneuzerling.com
As of 2023 the material in this post no longer functions due to changes in GitHub Actions. Machine learning models get stuck at the deployment stage all the time. This stuff is hard. GitHub Actions is a tool for automating tasks associated with a repository. I wanted to see if I could implement some sort of end-to-end automatic training, deployment and execution of a model. And I’m going to use R because people keep telling me that this sort of stuff can’t be done with R.| mdneuzerling.com