# run-r-on-amls

**Repository Path**: mirrors_microsoft/run-r-on-amls

## Basic Information

- **Project Name**: run-r-on-amls
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Not specified
- **Default Branch**: master
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2020-08-19
- **Last Updated**: 2026-05-30

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# Run R scoring scripts on Azure Machine Learning Services

>**DEPRECATION WARNING**: The official AzureML SDK for R has meanwhile become available, so the code included in this repo might be obsolete for your case.
> See [here](https://github.com/Azure/azureml-sdk-for-r) for more details on the new R SDK.

This sample shows how to operationalize R models in Azure Machine Learning Services (AMLS).

1. If not done yet, you have to setup/configure Azure Machine Learning Services first. See [here](https://github.com/timoklimmer/setup-machine-for-amls/blob/master/How%20To%20Setup%20Your%20Machine%20for%20Azure%20Machine%20Learning%20Services.ipynb) for details.

2. Once done, you can create a model using the `create_model.r` script, eg. in RStudio or any other IDE you prefer.

3. Finally, use the `create-webservice.ipynb` notebook to create and deploy the webservice with your R model.

4. To see an example using plain REST (from Python), check the `consume-webservice.ipynb`.

Enjoy - and as always: feel free to use but don't blame me if things go wrong ;-)