Merge pull request #2301 from kraigb/kraigb-feedback

Update references to Microsoft R Server

Author: v-shils

docs/rtvs/check-for-update.md

@@ -26,4 +26,4 @@ For Visual Studio 2015, the most recent installer is available from [aka.ms/rtvs
 
 See [Installation](installing-r-tools-for-visual-studio.md) for Visual Studio and Windows requirements.
 
-For updates on Microsoft R Open and Microsoft R Server, see: [Microsoft R products.](http://aka.ms/rtvs-msft-r)
+For updates on Microsoft R Open and Microsoft Machine Learning Server (formerly Microsoft R Server), see: [Microsoft R products.](http://aka.ms/rtvs-msft-r)

docs/rtvs/faq.md

@@ -84,9 +84,9 @@ Q. **What is Remote Services in Visual Studio?**
 
 A. Remote R Services for Visual Studio allows you to set up Windows or Linux machine and then connect to it from RTVS. See [Set up remote workspaces](setting-up-remote-r-workspaces.md).
 
-Q. **Can RTVS connect to Microsoft R Server?**
+Q. **Can RTVS connect to Microsoft Machine Learning Server?**
 
-A. No, because Microsoft R Server is a different technology and does not provide same connectivity mechanism as required by RTVS.
+A. No, because Microsoft ML Server is a different technology and does not provide same connectivity mechanism as required by RTVS.
 
 Q. **Can RTVS connect to a VM created using the Data Science VM image on Azure?**
 

docs/rtvs/getting-started-samples.md

@@ -14,13 +14,13 @@ ms.workload:
 
 # R Tools for Visual Studio sample projects
 
-This collection of samples gets you started on R, R Tools for Visual Studio (RTVS), and Microsoft R Server:
+This collection of samples gets you started on R, R Tools for Visual Studio (RTVS), and Microsoft Machine Learning Server:
 
 1. Download the [samples zip file](https://github.com/Microsoft/RTVS-docs/archive/master.zip) and extract to a folder of your choice.
 1. Open `examples/Examples.sln` to see two folders in the project:
 
     - *A First Look at R* gives a gentle introduction for newcomers to R.
-    - *MRS and Machine Learning* gives examples of how to use R and Microsoft R Server for machine learning.
+    - *MRS and Machine Learning* gives examples of how to use R and Microsoft Machine Learning Server for machine learning.
 
 ## A First Look at R
 
@@ -34,52 +34,52 @@ This sample provides an in-depth introduction to R through the extensive comment
 
     ![Example output from the 2-Introduction to ggplot2.R sample](media/samples-ggplot-output.png)
 
-## Microsoft R Server and Machine Learning
+## Microsoft Machine Learning Server and Machine Learning
 
-This collection of examples shows how to use R to create machine learning models and to take advantage of [Microsoft R Server (MRS)](http://aka.ms/rtvs-msft-r). Install MRS to run scripts with `MRS` in the title and where noted.
+This collection of examples shows how to use R to create machine learning models and to take advantage of [Microsoft Machine Learning Server](/machine-learning-server/what-is-machine-learning-server).
 
 As with all examples, open the file, place the cursor at the top, and then step through the code line by line with **Ctrl**+**Enter**. The markdown files in each folder also contain additional details.
 
 - `Benchmarks` runs a number of intensive, parallel linear algebra computations to show the performance gains that are possible through the use of Microsoft R Open and the Intel Math Kernel Library (MKL). With simulated data, the benchmarks specifically compare matrix calculations on one thread versus two.
 
     ![Example benchmark plot](media/samples-mro-benchmark-plot.png)
 
-- `Bike_Rental_Estimation_with_MRS` creates a demand prediction model for bike rentals based on a historical data set, using Microsoft R Server. 
+- `Bike_Rental_Estimation_with_MRS` creates a demand prediction model for bike rentals based on a historical data set, using Microsoft ML Server. 
 
 - `Data_Exploration` contains three scripts:
 
   - `Import Data from URL.R` shows how to load a URL-identified data file into R.
-  - `Import Data from URL to xdf.R` shows how to load a URL-identified data file into Microsoft R Server as an xdf. (Requires MRS.)
+  - `Import Data from URL to xdf.R` shows how to load a URL-identified data file into Microsoft ML Server as an xdf.
   - `Using ggplot2.R` is an extension of the `A First Look at R/2-Introduction to ggplot2.R` sample, giving a more extensive tour of ggplot2's functionality including interactive 3D plotting.
 
       ![Output of Using ggplot2.R example](media/samples-3d-interactive.png)
 
 - `Datasets` contains three *.csv* files used by other samples
 - `Flight_Delays_Prediction_with_R` and `Flight_Delays_Prediction_with_MRS` shows how to predict flight delays using R, machine learning, and historical on-time performance and weather data. 
-- `Machine learning` contains three samples for learning to predict flight delays, housing prices, and bike rentals. Together, these samples demonstrate the application of R and MRS to real-world problems. They also show you how to use several popular machine learning models and deploy them as an Azure Web Service using an [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/) workspace.
+- `Machine learning` contains three samples for learning to predict flight delays, housing prices, and bike rentals. Together, these samples demonstrate the application of R and Microsoft ML Server to real-world problems. They also show you how to use several popular machine learning models and deploy them as an Azure Web Service using an [Azure Machine Learning](https://azure.microsoft.com/services/machine-learning/) workspace.
 
-- `R_MRO_MRS_Comparison` is a six-part comparison that shows the similarities and differences of R, Microsoft R Open and Microsoft R Server with commands, syntax, constructs, and performance.
+- `R_MRO_MRS_Comparison` is a six-part comparison that shows the similarities and differences of R, Microsoft R Open and Microsoft ML Server with commands, syntax, constructs, and performance.
 
-## What's special about Microsoft R Open and Microsoft R Server?
+## What's special about Microsoft R Open and Microsoft ML Server?
 
 [Microsoft R Open](http://aka.ms/rtvs-r-open), Microsoft's distribution of R, is different from [CRAN R](https://cran.r-project.org/) in two important ways:
 
 1. [Better computation performance](https://mran.revolutionanalytics.com/rro/#intelmkl1) when used with the [Intel Math Kernel Libraries](https://software.intel.com/intel-mkl). The libraries are available as a free download from Microsoft for use with Microsoft R Open.
 
 1. [Reproducible R Toolkit](https://mran.revolutionanalytics.com/rro/#reproducibility) ensures that the libraries you used to build your R program are always available to others that want to reproduce your work.
 
-[Microsoft R Server](http://aka.ms/rtvs-msft-r) is an extension of R that allows you to handle more data and handle it faster. It gives R two powerful capabilities:
+[Microsoft ML Server (MLS)](/machine-learning-server/what-is-machine-learning-server) is an extension of R that allows you to handle more data and handle it faster. It gives R two powerful capabilities:
 
-1. Larger data sets without RAM limitations. MRS can process out-of-memory data from a variety of sources including Hadoop clusters, databases, and data warehouses.
+1. Larger data sets without RAM limitations. ML Server can process out-of-memory data from a variety of sources including Hadoop clusters, databases, and data warehouses.
 
-1. Parallel, multi-core processing. MRS can efficiently distribute computation across all the computational resources it has available. On your personal workstation or a remote cluster, MRS gets an answer faster.
+1. Parallel, multi-core processing. MLS can efficiently distribute computation across all the computational resources it has available. On your personal workstation or a remote cluster, MLS gets an answer faster.
 
-The following comparison shows that MRS and MRO with MKL have significantly better computation performance related to certain matrix calculation than R and MRO without MKL. Simulated data is used in this calculation:
+The following comparison shows that MLS and MRO with MKL have significantly better computation performance related to certain matrix calculation than R and MRO without MKL. Simulated data is used in this calculation:
 
-![Comparing MRS and MRO with MKL to R and MRO without MKL](media/samples-speed-comparison.png)
+![Comparing MLS and MRO with MKL to R and MRO without MKL](media/samples-speed-comparison.png)
 
-For a technical comparison of R with MRO and MRS, check out [Lixun Zhang's detailed discussion](http://htmlpreview.github.io/?https://github.com/lixzhang/R-MRO-MRS/blob/master/Introduction_to_MRO_and_MRS.html) on the topic.
+For a technical comparison of R with MRO and MLS, check out [Lixun Zhang's detailed discussion](http://htmlpreview.github.io/?https://github.com/lixzhang/R-MRO-MRS/blob/master/Introduction_to_MRO_and_MRS.html) on the topic.
 
-The following figure then compares elapsed time in seconds used in building Logistic Regression models to predict flight delays greater than 15 minutes.  Elapsed time used in CRAN R increases dramatically when increasing a small number of rows, while MRS increases only by approximately two times. For details of this benchmark, check out the *Benchmarks/rxGlm_benchmark.R* example.
+The following figure then compares elapsed time in seconds used in building Logistic Regression models to predict flight delays greater than 15 minutes.  Elapsed time used in CRAN R increases dramatically when increasing a small number of rows, while MLS increases only by approximately two times. For details of this benchmark, check out the *Benchmarks/rxGlm_benchmark.R* example.
 
 ![rxGlm benchmark](media/samples-rxGLM-benchmark.png)