Hi @AntonDBUser,

Please refer to these posts for R Shiny using Databricks Apps - https://docs.databricks.com/en/sparkr/shiny.html#id8, https://community.databricks.com/t5/machine-learning/host-r-shiny-app/m-p/22992

For R Models hosting, we recommend using Posit Connect.

• Useful Resources
  Parallelize R code using user-defined functions (UDFs) in sparklyr - Posit

  • Install the necessary packages and set up your credentials, it remotely access Databricks cluster through R Studio

  • Use sparklyr to access your Databricks cluster in RStudio.With sparklyr, you can execute distributed R code within the Spark environment in Databricks cluster.

  • Train a machine learning model and run predictions using spark udf

  • Save the trained model as an R object

  • Upload the R object to Databricks, save it as a pin, or store it in another suitable location

  • Use the vetiver package to store the model as a pin and deploy it as a REST API on Posit Connect

• Other blogs

  • Databricks and Posit announce new integrations, simplifying Lakehouse access for developers

  • From Databricks to Posit Connect: Building and Deploying a Shiny for R Application\

While it is technically possible to serve R models without Posit Connect, it involves several complex adjustments.

Below are the steps and options available:

1. Create a MLflow Run and Log the R Model and get a MLflow URI:

• Use the 'crate' flavor to log the R model within an experiment

• Include a list of required package versions or an renv lockfile.

2. Add Additional Artifacts:

• Create a `pyfunc` that wraps the R model

•   Copy the model artifacts into a new model.

•   Generate `conda.yml`.

•   Adjust the `conda_env.yaml` to include `r-base` to add R to the container image.

•   Detect the version of R.

•   Use `rpy2` to set up the predict method and pass parameters to it.

•  The `pyfunc` must use `rpy2` to load and invoke the R model.