With the launch of Microsoft R Server 9.1, many optimizations and new features were delivered to our users. One key feature is interoperability between Microsoft R Server and sparklyr. It allows users to utilize Spark as the backend for dplyr, one of the most popular data manipulation packages. Another key feature is it allows users the ability to use Spark integrated Machine Learning algorithms directly from within R. For H2O users, the Microsoft R Server sparklyr Interop can be used to covert sparklyr data frames to H2O data frames. This allows data imported from Microsoft R Server to be used with H2O modelling and data partitioning algorithms, via the rsparkling package.

Microsoft R Server 9.0, Microsoft's R distribution with added big-data, in-database, and integration capabilities, was released today and is now available for download to MSDN subscribers. This latest release is built on Microsoft R Open 3.3.2, This release includes a brand new R package for machine learning: MicrosoftML. This package provides state-of-the-art, fast and scalable machine learning algorithms for common data science tasks including featurization, classification and regression. Fast linear and logistic model functions based on the Stochastic Dual Coordinate Ascent method; Fast Forests, a random forest and quantile regression forest implementation based on FastRank, an efficient implementation of the MART gradient boosting algorithm; A neural network algorithm with support for custom, multilayer network topologies and GPU acceleration; One-class anomaly detection based on support vector machines. One-class anomaly detection based on support vector machines.

Built by data scientists, the R programming language has always been a tool for data scientists. But Microsoft's R Server 9, the first full new version of the commercial package of R since Microsoft bought the company that created this distribution, Revolution Analytics, is also now aimed at a new audience -- enterprise customers who have developers and analysts as well as data scientists. That makes working with data from a wider range of sources key because enterprises have such mixed environments these days. R Server already supported Apache Spark 1.6 data processing framework; R Server 9 (which is built on open source R 3.3.2) adds support for Spark 2.0, so you can take advantage of the new options for working with streaming data and the improved memory management subsystem. "You can intermix calls to massively parallel algorithms in R with calls to native Spark, through the SparkR library," explained Bill Jacobs, Principal Program Manager on the R Server team.

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Expose R models as web services: Convert R models and scripts into web services with just a single line of code, and do so directly from your favorite IDE such as R Tools for Visual Studio (RTVS), RStudio, or Jupyter Notebooks. R models do not have to be translated from R to the language of the Line of Business (LoB) application. Integrate more easily: With the simplified application integration experience offered by Swagger, R models can be consumed by any application written in any programming language. Write once and deploy in multiple platforms: Models can be trained in one environment and deployed to a different environment, on premises or in the cloud, resulting in big savings of time and money. Ensure high availability: Use the active-active high availability and grid computing capabilities of MRS to scale predictive applications with your business needs.