Microsoft has updated the Data Science Virtual Machine, a data science toolkit-in-a-box that you can easily spin up on the Microsoft Azure cloud service. The virtual machine now comes pre-configured with Microsoft R Server Developer Edition (upgraded from Microsoft R Open), Anaconda Python, Jupyter notebooks for Python and R, Visual Studio Community Edition, Power BI desktop, and SQL Server Express edition.
For R users, this is a great way to harness some powerful hardware for heavy-duty computations with R. Microsoft R Server includes RevoScaleR, the R package that allows you to build statistical models on data larger than available RAM. To develop R code with Microsoft R Server, you can either use the Revolution R Enterprise 8.0 IDE or use a Jupyter Notebook. You can also create dashboards including the output from R code using PowerBI desktop.
Based on Windows Server 2012, the Data Science Virtual Machine is available in three sizes: A3 (4 cores with 7GB RAM); A4 (8 cores/14Gb) and A7 (8 cores/56Gb). Hourly usage prices start USD$0.36 (and if you're new to Azure, you can get started with an Azure free trial.) Provisioning your machine takes just a few minutes, and once it's running you can use it just as you do your current Windows machine via the standard Remote Desktop Connection tool — no separate VM client is required.
There's lots more you can do with your Data Science Virtual Machine as well. For information on how to use it to develop Python applications, deploy R models to Azure ML, share code you develop via Github and much more, follow the link below.
Microsoft Azure: Ten things you can do on the Data science Virtual Machine