Visualize model predictions via a Shiny app.Īlong the way, here are three specific times that a pin is going to come in handy:.Serve model predictions via a Plumber API.Train and deploy a model of future bike availability.Combine the station metadata with raw data on bike availability from the data lake to create an analysis dataset.Ingest metadata about the stations, like name and location, from the bike data API.To get there, I’m going to follow this analysis workflow: All of the code for this demo is available on Github.

I’m going to make model predictions available in production by providing programmatic access to the model via an API and to humans via a Shiny app. Capital Bikeshare makes data on the current availability of bikes at each station available via a public API. The model will predict future availability of bicycles at Capital Bikeshare docks, which provide short-term bicycle rentals in and around Washington DC. Along the way, I’ll make extensive use of pins for important parts of my workflow. In this article, I’ll create a predictive model, programmatically serve predictions via a Plumber API, and visualize those predictions in a Shiny app. Especially when the object is reused several times or updated independently from the rest of the analysis, a pin is probably a better solution than saving a file with your code. In general, pins are a good substitute for saving objects alongside analysis code as.

Shiny from rstudio