<p><div><div><div><div data-width="100" data-show-faces="false" data-send="true" data-layout="button_count" data-height="21" data-href="https://www.r-bloggers.com/microsoft-r-server-9-0-now-available/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2016/12/microsoft-r-server-90-now-available.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<div>
<p><a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/">Microsoft R Server</a> 9.0, Microsoft's R distribution with added big-data, in-database, and integration capabilities, was <a target="_blank" rel="nofollow" href="https://blogs.technet.microsoft.com/machinelearning/2016/12/07/introducing-microsoft-r-server-9-0/">released today</a> and is now <a target="_blank" rel="nofollow" href="https://aka.ms/rserver9.0.1">available for download to MSDN subscribers</a>. This latest release is built on <a target="_blank" rel="nofollow" href="http://blog.revolutionanalytics.com/2016/11/mro-332-now-available.html">Microsoft R Open 3.3.2</a>, and adds new machine-learning capabilities, new ways to integrate R into applications, and additional big-data support for Spark 2.0.</p>
<p>This release includes a brand new R package for machine learning: <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/microsoftml-introduction">MicrosoftML</a>. This package provides state-of-the-art, fast and scalable machine learning algorithms for common data science tasks including featurization, classification and regression. Some of the <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/microsoftml/microsoftml">functions provided</a> include:</p>
<ul>
<li>Fast linear and logistic model functions based on the <a target="_blank" rel="nofollow" href="http://research.microsoft.com/en-us/um/people/mbilenko/papers/15-sasdca.pdf">Stochastic Dual Coordinate Ascent</a> method;</li>
<li>Fast Forests, a random forest and quantile regression forest implementation based on <a target="_blank" rel="nofollow" href="http://projecteuclid.org/DPubS?service=UI&version=1.0&verb=Display&handle=euclid.aos/1013203451">FastRank</a>, an efficient implementation of the MART gradient boosting algorithm;</li>
<li>A neural network algorithm with support for custom, multilayer network topologies and GPU acceleration;</li>
<li>One-class anomaly detection based on <a target="_blank" rel="nofollow" href="https://www.microsoft.com/en-us/research/publication/estimating-the-support-of-a-high-dimensional-distribution/">support vector machines</a>.</li>
</ul>
<p>You can learn more about MicrosoftML at this <a target="_blank" rel="nofollow" href="https://info.microsoft.com/intro-to-microsoftml.html">live webinar on Wednesday, December 14</a>.</p>
<p>The <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/scaler/scaler">RevoScaleR</a> package, which provides big-data support for Microsoft R Server, has been updated to support Spark 2.0 (This is in addition to existing support for in-database computations with SQL Server and Teradata.) New functions allow you to connect to a Hive, Parquet or Spark DataFrame data source, and execute data manipulation and predictive modeling tasks directly on the data as it resides within Spark.</p>
<p>Microsoft R Server also sports new capabilities to integrate R functions into other applications. (This is the results of enhancements to the former DeployR project, which is now part of Microsoft R Server.) Microsoft R Server can now host R functions exposed as web services: data scientists can publish R functions to the R Server, which can then be accessed from any application. Application developers can integrate those R functions into their applications using <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/operationalize/api">easy-to-consume Swagger-based APIs</a> from any programming language.</p>
<p><a target="_blank" rel="nofollow" href="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01bb095cd731970d-pi"><img title="R Client" alt="R Client" src="http://revolution-computing.typepad.com/.a/6a010534b1db25970b01bb095cd731970d-800wi" border="0"></a></p>
<p>Also released today is the free <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/r-client-get-started">Microsoft R Client 3.3.2</a>. This is a desktop edition of Microsoft R Server 9.0, designed for local data science development and remote execution on local servers and in the cloud. It includes the same MicrosoftML package as Microsoft R Server. The RevoScaleR package in Microsoft R Client is limited in data size when computing locally, but can shift computations to a remote Microsoft R Server when working with larger data sets. It also includes the new <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/mrsdeploy/mrsdeploy">mrsdeploy package</a>, so you can <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/operationalize/remote-execution">remote-execute any R code</a> on the remote R Server, or even publish R functions as a Web service.</p>
<p>For a complete list of changes, see <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/rserver-whats-new">What's New in R Server 9.0.1</a>. And for more on the release of Microsoft R Server 9.0, check out the blog post linked below.</p>
<p>Cortana Intelligence and Machine Learning Blog: <a target="_blank" rel="nofollow" href="https://blogs.technet.microsoft.com/machinelearning/2016/12/07/introducing-microsoft-r-server-9-0/">Introducing Microsoft R Server 9.0</a></p>
</div>
<p>
<div>
<div>To <strong>leave a comment</strong> for the author, please follow the link and comment on their blog: <strong><a href="http://blog.revolutionanalytics.com/2016/12/microsoft-r-server-90-now-available.html"> Revolutions</a></strong>.</div>
<hr>
<a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers.com</a> offers <strong><a rel="nofollow" href="https://feedburner.google.com/fb/a/mailverify?uri=RBloggers">daily e-mail updates</a></strong> about <a title="The R Project for Statistical Computing" rel="nofollow" href="https://www.r-project.org/">R</a> news and <a title="R tutorials" rel="nofollow" href="https://www.r-bloggers.com/search/tutorial">tutorials</a> on topics such as: <a title="Data science" rel="nofollow" href="https://www.r-bloggers.com/search/data%20science">Data science</a>, <a title="Big Data" rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data">Big Data, <a title="R jobs" rel="nofollow" href="https://www.r-users.com/">R jobs</a>, visualization (<a title="ggplot and ggplot2 tutorials" rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2">ggplot2</a>, <a title="Boxplots using lattice and ggplot2 tutorials" rel="nofollow" href="https://www.r-bloggers.com/search/boxplot">Boxplots</a>, <a title="Maps and gis" rel="nofollow" href="https://www.r-bloggers.com/search/map">maps</a>, <a title="Animation in R" rel="nofollow" href="https://www.r-bloggers.com/search/animation">animation</a>), programming (<a title="RStudio IDE for R" rel="nofollow" href="https://www.r-bloggers.com/search/RStudio">RStudio</a>, <a title="Sweave and literate programming" rel="nofollow" href="https://www.r-bloggers.com/search/sweave">Sweave</a>, <a title="LaTeX in R" rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX">LaTeX</a>, <a title="SQL and databases" rel="nofollow" href="https://www.r-bloggers.com/search/SQL">SQL</a>, <a title="Eclipse IDE for R" rel="nofollow" href="https://www.r-bloggers.com/search/eclipse">Eclipse</a>, <a title="git and github, Version Control System" rel="nofollow" href="https://www.r-bloggers.com/search/git">git</a>, <a title="Large data in R using Hadoop" rel="nofollow" href="https://www.r-bloggers.com/search/hadoop">hadoop</a>, <a title="Web Scraping of google, facebook, yahoo, twitter and more using R" rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping">Web Scraping</a>) statistics (<a title="Regressions and ANOVA analysis tutorials" rel="nofollow" href="https://www.r-bloggers.com/search/regression">regression</a>, <a title="principal component analysis tutorial" rel="nofollow" href="https://www.r-bloggers.com/search/PCA">PCA</a>, <a title="Time series" rel="nofollow" href="https://www.r-bloggers.com/search/time+series">time series</a>, <a title="finance trading" rel="nofollow" href="https://www.r-bloggers.com/search/trading">trading</a>) and more...
<img height="1" alt="" width="1" src="http://feeds.feedburner.com/~r/RBloggers/~4/97yhWMtpb6o"></a></div>