r-model-template

The goal of this repository is to act as a template demonstrating a full end to end MLOps style R project.

The toy example is training a predictive model on a csv dataset that predicts water temperature throughout the year for Sydney beaches. This project addresses how you might want to structure this project to run in a ‘Production’ context.

I go into more details in my workshop Data Science Workflows in R An introduction to deploying production quality R code.

This includes:

• Data import and cleaning with the {tidyverse}
• Data documentation
• Documented R functions for processing and model training using {roxygen2}
• R package structure
• Model training and inference pipeline using {quarto}
• Unit tests with {testthat}
• Monitoring dashboard in interactive rmarkdown and {flexdashboard}
• Model card for documenting model ethics, decision and recommended use cases
• Prediction API endpoint creation using {plumber}
• Model versioning and metadata storage using {vetiver} and {pins}
• Fully containerized deployment using Docker
• Dependency managment using {renv}
• Project website and documentation using {pkgdown}
• Continuous Integration (CI) pipeline automated using Github Actions

Installation

You can install the development version of beachwatch from GitHub with:

# install.packages("pak")
pak::pak("deanmarchiori/r-model-template")

Then run devtools::install()

The model pipeline can be stepped through in model_notebook.qmd

Example

This is a basic example of using the deployed outputs from this project.

1. Clone the repository: git clone https://github.com/deanmarchiori/r-model-template.git

2. To build your docker image: docker build -t beach .

3. To run it: docker run -p 8080:8080 beach

The endpoint should now be live at http://127.0.0.1:8080

Deployment

To deploy this in a production context, the project can be easily extended to add a Continuous Deployment (CD) workflow in Github Action to build and push the Docker container to your preferred container hub, which can then be accessed and run by your production systems.