Model Deployment and Plumber

This resource showcases how to expose a machine learning model built in R via a Plumber API. A model is first trained and saved as an .rds file. The Plumber file loads this saved model from disk, interprets data submitted via POST request, and returns a JSON object containing predictions based on the input data. A simple Shiny app is provided to interact with the API. Click here for source code.

Model #

Train a simple model that predicts MPG.

cars_model <- lm(mpg ~ cyl + hp, data = mtcars)
saveRDS(cars_model, here::here("R", "model-api", "cars-model.rds"))


Build an API that predicts MPG based on user inputs and the trained model.

API [Login] to RStudio Connect.

Prediction app #

Use a Shiny app to predicts MPG based from the API.

App [Login] to RStudio Connect.

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