<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-9-1-now-available/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2017/04/microsoft-r-server-91-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>During today's <a target="_blank" rel="nofollow" href="https://www.microsoft.com/en-us/sql-server/data-amp">Data Amp</a> online event, Joseph Sirosh <a target="_blank" rel="nofollow" href="https://blogs.technet.microsoft.com/dataplatforminsider/2017/04/19/introducing-microsoft-r-server-9-1-release/">announced the new Microsoft R Server 9.1</a>, which is <a target="_blank" rel="nofollow" href="https://my.visualstudio.com/Downloads?pid=2182">available for customers now</a>. In addition the updated <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/r-client-get-started">Microsoft R Client</a>, which has the same capabilities for local use, is available free for everyone on both Windows and — new to this update — Linux.</p>
<p>This release adds many new capabilities to Microsoft R, including: </p>
<p><strong>Based on R 3.3.3. </strong>This update is based on the latest release of open source R from the <a target="_blank" rel="nofollow" href="https://www.r-project.org/foundation/">R Foundation</a>, bringing many <a target="_blank" rel="nofollow" href="https://mran.microsoft.com/news/#r333">improvements</a> to the core language engine.</p>
<p><strong>Microsoft R Client now available on Linux.</strong> The Microsoft R Client is available free, and has all of the same capabilities as Microsoft R Server for a standalone machine (or, you can use it to push heavy workloads to <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/operationalize/remote-execution">a remote Microsoft R Server</a>, <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/sql-server-r-services">SQL Server</a> or <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/scaler-distributed-computing-compute-context">compute cluster</a>). With this release it is now also <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/r-client-install-linux">available on Linux</a> (specifically, Ubuntu, Red Hat / CentOS and SUSE) in addition to <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/r-client-install-windows">Windows</a>.</p>
<p><strong>New function for &quot;pleasingly parallel&quot; R computations on data partitions</strong>. The new <a target="_blank" rel="nofollow" href="https://blogs.msdn.microsoft.com/rserver/2017/04/12/pleasingly-parallel-using-rxexecby/"><code>rxExecBy</code> function</a> allows you to apply any R function to partitions of a data set, and compute on the partitions in parallel. (You don't have to manually split the data first; this happens automatically at the data source, without the need to move or copy the data.) You can use any parallel platform supported in Microsoft R: multiple cores in a local context, multiple threads in SQL Server, or multiple nodes in a Spark cluster.</p>
<p><strong>New functions for sentiment scoring and image featurization</strong> have been added to the built-in MicrosoftML package. The new <code><a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/microsoftml/packagehelp/getsentiment">getSentiment</a></code> function returns a <a target="_blank" rel="nofollow" href="https://blogs.msdn.microsoft.com/rserver/2017/04/14/get-sentiment-with-a-pre-trained-model/">sentiment score</a> (on a scale from &quot;very negative&quot; to &quot;very positive&quot;) for a piece of English text. (You can also <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/microsoftml/packagehelp/featurizetext">featurize</a> text in English, French, German, Dutch, Italian, Spanish and Japanese as you build your own models.) The new <code><a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/microsoftml/packagehelp/featurizeimage">featurizeImage</a></code> function decomposes an image into a few numeric variables (using a selection of <a target="_blank" rel="nofollow" href="http://www.cv-foundation.org/openaccess/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html">ResNet recognizers</a>) which can then be <a target="_blank" rel="nofollow" href="https://blogs.msdn.microsoft.com/rserver/2017/04/12/image-featurization-with-a-pre-trained-deep-neural-network-model/">used as the basis of a predictive model</a>. Both functions are based on deep neural network models generated from thousands of compute-hours of training by Microsoft Research.</p>
<p><strong>Interoperability between Microsoft R Server and sparklyr</strong>. You can now use RStudio's <a target="_blank" rel="nofollow" href="http://spark.rstudio.com/">sparklyr</a> package <a target="_blank" rel="nofollow" href="https://blogs.msdn.microsoft.com/microsoftrservertigerteam/2017/04/19/new-features-in-9-1-microsoft-r-server-with-sparklyr-interoperability/">in tandem with Microsoft R Server</a> in a single Spark session</p>
<p><strong>New machine learning models in Hadoop and Spark</strong>. The new <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/microsoftml/microsoftml">machine learning functions</a> introduced with Version 9.0 (such as <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/microsoftml/packagehelp/rxfasttrees">FastRank gradient-boosted trees</a> and <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/microsoftml/packagehelp/neuralnet">GPU-accelerated deep neural networks)</a> are <a target="_blank" rel="nofollow" href="https://blogs.msdn.microsoft.com/rserver/2017/04/19/microsoft-ml-on-spark-and-hadoop/">now available in the Hadoop and Spark contexts</a> in addition to standalone servers and within SQL Server. </p>
<p><strong>Production-scale deployment of R models. </strong>The new <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/operationalize/data-scientist-manage-services"><code>publishService</code> function</a> creates a real-time web-service for certain RevoScaleR and MicrosoftML functions that is independent of any underlying R interpreter, and can return results with <a target="_blank" rel="nofollow" href="https://blogs.msdn.microsoft.com/rserver/2017/04/19/whats-new-in-r-server-9-1-operationalization/">millisecond latency</a>. There are also new tools for launching and managing <a target="_blank" rel="nofollow" href="https://msdn.microsoft.com/en-us/microsoft-r/operationalize/data-scientist-batch-mode">asynchronous batch R jobs</a>.</p>
<p><strong>Updates for R in SQL Server</strong>. There are also improvements for R Services in SQL Server 2016, in addition to the new R functions described above. There are new tools to <a target="_blank" rel="nofollow" href="https://docs.microsoft.com/en-us/sql/advanced-analytics/r/r-package-management-for-sql-server-r-services">manage installed R packages on SQL Server</a>, and faster single-row scoring. Even more new capabilities are coming in <a target="_blank" rel="nofollow" href="https://www.microsoft.com/en-us/sql-server/sql-server-2017">SQL Server 2017</a> (now in <a target="_blank" rel="nofollow" href="https://www.microsoft.com/en-us/sql-server/sql-server-2017#resources">preview</a> for both Windows and Linux), including the ability to use both R and Python for in-database computations.</p>
<p>For more on the updates in Microsoft R Server 9.1, check the official blog post by Nagesh Pabbisetty linked below.</p>
<p>SQL Server Blog: <a target="_blank" rel="nofollow" href="https://blogs.technet.microsoft.com/dataplatforminsider/2017/04/19/introducing-microsoft-r-server-9-1-release/">Introducing Microsoft R Server 9.1 release</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/2017/04/microsoft-r-server-91-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/ULoXdke1ZPg"></a></div>