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R at Microsoft

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Presented by David Smith, March 21 2016
How Microsoft uses R, integrates R, and supports the R community and the R project.

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R at Microsoft

  1. 1. Historical black box sensor records and maintenance events for many aircraft Train and compare various models to predict maintenance events Scoring rules to predict likely maintenance events from sensor data
  2. 2. mran.microsoft.com
  3. 3. msdsug.microsoft.com
  4. 4. cloud computing 2011  2016 5x increase data science Universities filling 300,000 US talent gap 90% of the data in the world today has been created in the last two years alone big data open source including R, Python, Linux, Hadoop, Spark, …
  5. 5. Thank you! David Smith R Community Lead Microsoft @revodavid davidsmi@microsoft.com Revolutions blog blog.revolutionanalytics.com blog.revolutionanalytics.com/2016/02/xbox_usage_trends_r.html www.microsoft.com/en-us/stories/88acres powerbi.microsoft.com/en-us/industries/airline blog.revolutionanalytics.com/2016/03/sql-server-2016-launch.html studio.azureml.net github.com/RevolutionAnalytics/AzureML blog.revolutionanalytics.com/2016/03/scoring-r-models-with-excel.html www.visualstudio.com/en-us/features/rtvs-vs.aspx mran.microsoft.com/download
  6. 6. Microsoft R Server Big-data analytics and distributed computing on Linux, Hadoop and Teradata SQL Server 2016 Big-data analytics integrated with SQL Server database (coming soon) PowerBI Computations and charts from R scripts in dashboards Azure ML Studio R Scripts in cloud-based Experiment workflows Visual Studio R Tools for Visual Studio: integrated development environment for R (coming soon) HDInsights R integrated with cloud-based Hadoop clusters Cortana Analytics Cloud-based R APIs and Virtual Machines

Editor's Notes

  • http://blog.revolutionanalytics.com/2016/02/xbox_usage_trends_r.html
    http://blog.revolutionanalytics.com/2014/05/microsoft-uses-r-for-xbox-matchmaking.html
  • https://www.microsoft.com/en-us/stories/88acres/

    Energy prediction and load shaping for buildings
     
    Energy costs for Microsoft’s 120 building main-campus are very high, particularly because of the almost exclusive usage of electric heating there. About 10% of these are demand charges (a peak-usage surcharge), and become very pronounced in the winter. To reduce these, we have modeled building energy consumption to predict demand peaks using random forest and boosted trees regression as implemented in the randomForest and gbm packages (sometimes together with caret) and then piloted in our operations center.
     
    Now in a second phase more advanced models were developed to allow this peak-flattening without manual intervention. Transitioning a predictive-model to a command-and-control model like this was complex, and capturing the physical reality required the use of multiple cascaded models, also using tree-based regression techniques. Optimization (to find the best control parameters) and simulation (to gauges the overall impact of intervention) were used and the problems typical for dynamical systems (stabilization, non-convergence, etc.) had to be overcome;  these will be addressed in the talks.
     
    All of the development and modelling work was done in R and Shiny using R-Studio and later RTVS, afterwards the R-code and ggplot2 plots were deployed to various platforms including Azure ML, PowerBI and R Services for SQL Server.
  • https://www.microsoft.com/en-us/server-cloud/products/sql-server-2016/
  • https://powerbi.microsoft.com/en-us/industries/airline
  • https://studio.azureml.net/
  • https://github.com/RevolutionAnalytics/AzureML
  • Details: http://blog.revolutionanalytics.com/2016/03/scoring-r-models-with-excel.html
  • https://www.visualstudio.com/en-us/features/rtvs-vs.aspx
  • Support for R
    R packages: AzureML, checkpoint, doParallel, Rhadoop, DeployR Open
    Full support for Linux in Azure, and now SQL
    Own Linux Distribution
    .NET open source
  • ×