Azure Machine Learning has added support for the R language, it was announced at the Ignite conference in Orlando this week.
A new R package azuremlsdk (available to install from Github now, and from CRAN soon), provides the interface to the Azure Machine Learning service. With R functions, you can provision new computing clusters in Azure, and use those to train models with R and deploy them as prediction endpoints for use from any app. You can also launch R-based notebooks in the new Azure Machine Learning studio web interface, or even launch a complete RStudio server instance on your cloud computing resources. Azure Machine Learning service supports the latest version of R (3.6.1) and all R packages (from CRAN, Github, or elsewhere). The video below from The AI Show demonstrates how it all works:
Azure Machine Learning is also great for teams that have both Python and R expertise. You can even call Python models from R (and vice-versa): in this Ignite 2019 talk (presented by me and Daniel Schneider) we deploy R and Python function as a container services, and call them both from a Shiny app. You can also find the slides and associated code from that talk in this Github repository.
To get started with R in Azure Machine Learning, a good place to start is the tutorial “Train and deploy your first model in R with Azure Machine Learning“. If you need an Azure subscription, use this link to sign up for Azure and get $200 in free Azure credits.
Azure Machine Learning: ml.azure.com