In the talk, I demonstrate the process in action (the demo starts at the 14:30 mark in the video below). I used Visual Studio Code to edit the
app.Rfile in repository, and then pushed the changes to GitHub. That immediately triggered the action to deploy the updated file via SSH to the Shiny Server, running in a remote VM. Similarly, changes to the data file or to the R script files implementing the logistic regression model would trigger the model to be retrained in the cluster, and re-deploy the endpoint to deliver new predictions from the updated model.
Click through for a quick summary, link to the repo, and embedded video of the talk.