Distributed Computing & What’s New

in R Server 9.0.1 Webinar

Better, Faster Machine Learning Development & Deployment

Recorded March 2017


Microsoft R Server (MRS) offers powerful features for enterprise-class advanced analytics across a variety of modern data platforms like SQL Server 2016, Hadoop, and Teradata. A key benefit includes allocating workloads intelligently across resources to achieve parallel and distributed computing – overcoming the memory-bound and single-threaded limitations of open-source R. Features added in the recent release of R Server 9.0.1 add even more value for the enterprise, with new machine learning algorithms, support for data science teams, and easier application deployment.


Check out the recording below from BlueGranite’s data science team for a demo and discussion of these exciting aspects of R Server!


Wistia video thumbnail - Distributed Computing What's New in R Server

Thanks for reporting a problem. We'll attach technical data about this session to help us figure out the issue. Which of these best describes the problem?

Any other details or context?


 Webinar GOALS

  • Provide a comparison of open-source R and Microsoft R Server

  • Demonstrate parallel computing with big data and R Server

  • Learn about new features in R Server 9.0.1 (released 12/2016) – including the new MicrosoftML package, and tools for remote execution and web service deployment

  • Learn how to get started with R Server


  • Recorded March 2017
  • Content is intended for Data Scientists, BI/DW and Analytics Managers, and anyone interested in Microsoft R


Colby Ford
Data ScientistColbyFord (002).jpg

Colby is a Data Scientist at BlueGranite. Coming from a background in mathematics, statistics, and bioinformatics, he combines this
expertise to bring Data Science to everyone. He utilizes R and Python and puts Machine Learning to work to gain insight from data. Outside of BlueGranite, Colby is an avid pianist and genomics researcher. Check out Colby’s website at www.colbyford.com.



Webinar - Parallel Processing | Mar 2017