Compare Machine Learning Server and Microsoft R products
The following table broadly compares members of the Machine Learning Server and Microsoft R. All products with R support are built on Microsoft R Open and install the package automatically.
There is no Python client or workstation version for Machine Learning Server. You can install the developer edition or an evaluation edition of Machine Learning Server if you require a free-of-charge copy for study and development.
|Machine Learning Server||Enterprise class server software||Commercial software||Fully supported by Microsoft||Adds custom functionality provided in proprietary Microsoft R and Python packages, intended for local, remote, or distributed execution of larger datasets at scale. This product was formerly known as Microsoft R Server.|
|Microsoft R Client||Workstation version of Microsoft R||Free||Community forums 1||Adds custom functionality provided in proprietary Microsoft R packages, intended for development and local execution of in-memory datasets.|
|Microsoft R Open||Microsoft's distribution of open-source R||Free||Community forums 1||Use Microsoft R Open as you would any other distribution of R. Script written against Microsoft R Open is straight R, composed of basic functions provided in publically available R packages.
Warning! Microsoft R Client and Machine Learning Server are built atop of specific versions of Microsoft R Open. If you are installing R Client or Machine Learning Server, always use the R Open version provided by the installer (or described in the installation guides) to avoid compatibility errors. You can also install and use Microsoft R Open on its own by downloading the R Open latest version.
For information on SQL Server Machine Learning Services, please visit the corresponding documentation here: https://docs.microsoft.com/en-us/sql/advanced-analytics/r/sql-server-r-services.
Compare features by product
Features provided by Machine Learning Server, Microsoft R Client, and Microsoft R Open can be categorized as shown in this table. This table slices key features by components. Additional capability provided in R Client and Machine Learning Server is delivered via proprietary packages.
|Microsoft R Open||Microsoft R Client||Machine Learning Server|
|Memory & Storage||Memory bound1||Memory bound1 & operates on large volumes when connected to R Server.||Memory across multiple nodes as well as data chunking across multiple disks.
Operates on bigger volumes & factors.
|Speed of Analysis||Multithreaded via MKL2 for non-RevoScaleR functions.||Multithreaded via MKL2 for non-RevoScaleR functions, but only up to 2 threads for ScaleR functions with a local compute context.||Full parallel threading & processing for revoscalepy and RevoScaleR functions as well as for non-proprietary functions (via MKL2) in both local and remote compute contexts.|
|Analytic Breadth & Depth||Open-source packages only.||Open-source R packages plus proprietary packages.||Open-source R packages plus proprietary packages with support for parallelization and distributed workloads. Proprietary Python packages for solutions written in that language.|
|Operationalizing R Analytics||Not available||Not available||Includes the instant deployment and easy consumption of R analytics, interactive remote code execution, speedy real-time scoring, scalability, and enterprise-grade security.|
1 Memory bound because product can only process datasets that fit into the available memory.
2 Because the Intel Math Kernel Library (MKL) is included in Microsoft R Open, the performance of a generic R solution is generally better. MKL replaces the standard R implementations of Basic Linear Algebra Subroutines (BLAS) and the LAPACK library with multithreaded versions. As a result, calls to those low-level routines tend to execute faster on Microsoft R than on a conventional installation of R.