However, instead of using local resources, the R environment runs on a VM in the cloud.

The environment will be familiar to anyone who has worked with R on a local workstation. If you want to get started with R in the cloud quickly and easily, this is your best bet. You can use the DSVM in two different ways: as an interactive workstation or as a compute platform for a custom cluster. The DSVM can be provisioned with either Windows or Linux as the operating system. It has many popular data science tools, including: The Data Science Virtual Machine (DSVM) is a customized VM image on Microsoft's Azure cloud platform built specifically for doing data science. Run R and Python scripts inside of the SQL Server database engine Offers a variety options for economically running R code across many nodes in a cluster This article covers the following Azure services that support the R language: ServiceĪ customized VM to use as a data science workstation or as a custom compute targetĬluster-based system for running R analyses on large datasets across many nodesĬollaborative Spark environment that supports R and other languagesĬloud service that you use to train, deploy, automate, and manage machine learning models Let's examine the various options and the most compelling scenarios for each one. By providing many different options for R developers to run their code in Azure, the company is enabling data scientists to extend their data science workloads into the cloud when tackling large-scale projects. Microsoft has fully embraced the R programming language as a first-class tool for data scientists. This article provides an overview of the various ways that data scientists can use their existing skills with the R programming language in Azure. Many data scientists dealing with ever-increasing volumes of data are looking for ways to harness the power of cloud computing for their analyses.

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