<p><div><div><div><div data-show-faces="false" data-height="21" data-width="100" data-layout="button_count" data-send="true" data-href="https://www.r-bloggers.com/services-and-tools-for-building-intelligent-r-applications-in-the-cloud/"></div></div><div></div></div><div></div></div>
<div>
(This article was first published on <strong><a href="http://blog.revolutionanalytics.com/2018/01/r-cloud-tools.html"> Revolutions</a></strong>, and kindly contributed to <a rel="nofollow" href="https://www.r-bloggers.com/">R-bloggers)</a> </div>
<div>
<p><em>by Le Zhang (Data Scientist, Microsoft) and Graham Williams (Director of Data Science, Microsoft)</em></p>
<p>As an in-memory application, R is sometimes thought to be constrained in performance or scalability for enterprise-grade applications. But by deploying R in a high-performance cloud environment, and by leveraging the scale of parallel architectures and dedicated big-data technologies, you can build applications using R that provide the necessary computational efficiency, scale, and cost-effectiveness.</p>
<p>We identify four application areas and associated <a rel="nofollow" href="https://github.com/yueguoguo/r-on-azure" target="_blank">applications and Azure services that you can use to deploy R in enterprise applications</a>. They cover the tasks required to prototype, build, and operationalize an enterprise-level data science and AI solution. In each of the four, there are R packages and tools specifically for accelerating the development of desirable analytics.</p>
<p>Below is a brief introduction of each.</p>
<h3>Cloud resource management and operation</h3>
<p>Cloud computing instances or services can be harnessed within an R session, and this favors programmatic control and operationalization of R based analytical pipelines. R packages and tools in this category are featured by offering a simplified way to interact with the Azure cloud platform and operate resources (e.g., blob storage, Data Science Virtual Machine, Azure Batch Service, etc.) on Azure for various tasks.</p>
<ul>
<li><a rel="nofollow" href="https://github.com/Microsoft/AzureSMR" target="_blank">AzureSMR</a> – R package for managing a selection of Azure resources. Targeted at data scientists who need to control Azure resources directly from R functions. APIs include <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/storage/blobs/storage-blobs-introduction/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Storage Blobs</a>, <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/hdinsight/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">HDInsight</a> (Nodes, Hive, Spark), <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/azure-resource-manager/resource-group-overview/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Resource Manager</a>, and <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/virtual-machines/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Virtual Machines</a>.</li>
<li><a rel="nofollow" href="https://github.com/Azure/AzureDSVM" target="_blank">AzureDSVM</a> – R package that offers a convenient harness for the <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Azure Data Science Virtual Machine</a> (DSVM), remote execution of scalable and elastic data science work, and monitoring of on-demand resource consumption.</li>
<li><a rel="nofollow" href="https://github.com/Azure/doAzureParallel" target="_blank">doAzureParallel</a> – R package that allows users to submit parallel workloads in Azure.</li>
<li><a rel="nofollow" href="https://github.com/Azure/rAzureBatch" target="_blank">rAzureBatch</a> – run R code in parallel across a cluster in <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/batch/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Azure Batch</a>.</li>
<li><a rel="nofollow" href="https://github.com/RevolutionAnalytics/AzureML" target="_blank">AzureML</a>– an R interface to <a rel="nofollow" href="https://studio.azureml.net/" target="_blank">AzureML</a> experiments, datasets, and web services.</li>
</ul>
<h3>Remote interaction and access to cloud resources</h3>
<p>Data scientists can seamlessly log in and out of R session on cloud for experimentation and explorative study. The R packages and tools in this category help data scientists or developers to remotely access or interact with Azure cloud instances or services for convenient development.</p>
<ul>
<li><a rel="nofollow" href="https://docs.microsoft.com/en-us/machine-learning-server/r-reference/mrsdeploy/mrsdeploy-package/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">mrsdeploy</a> – an R package that provides functions for establishing a remote session in a console application and for publishing and managing a web service that is backed by the R code block or script you provided.</li>
<li><a rel="nofollow" href="https://docs.microsoft.com/en-us/visualstudio/rtvs/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">R Tools for Visual Studio</a> – IDE with R support.</li>
<li><a rel="nofollow" href="https://www.rstudio.com/products/rstudio/#Server" target="_blank">RStudio Server</a> – IDE for remote R session with access via Internet browser.</li>
<li><a rel="nofollow" href="https://jupyterhub.readthedocs.io/en/latest/" target="_blank">JupterHub</a> – Jupyter notebook with multi-user access.</li>
<li><a rel="nofollow" href="https://github.com/IRkernel/IRkernel" target="_blank">IRKernel</a> – R kernel for Jupyter notebook.</li>
</ul>
<h3>Scalable and advanced analytics.</h3>
<p>Scalable analytics and advanced machine (deep) learning model creation can be performed in R on cloud services, with acceleration of application-specific hardware like GPUs. R packages and tools in this category allow one to perform large-scale R-based analytics on Azure with modern frameworks such as Spark, Hadoop, Microsoft Cognitive Toolkit, Tensorflow, and Keras. It is worth mentioning that many of the tools are pre-installed and configured for direct use on the <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/overview/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Azure Data Science Virtual Machine</a>.</p>
<h4>Scalable analytics</h4>
<ul>
<li><a rel="nofollow" href="https://github.com/RevolutionAnalytics/dplyrXdf" target="_blank">dplyrXdf</a> – a dplyr backend for the XDF data format used in <a rel="nofollow" href="https://docs.microsoft.com/en-us/machine-learning-server/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Microsoft ML Server</a>.</li>
<li><a rel="nofollow" href="http://spark.rstudio.com/" target="_blank">sparklyr</a> – R interface for Apache Spark.</li>
<li><a rel="nofollow" href="https://spark.apache.org/docs/latest/sparkr.html" target="_blank">SparkR</a> – an R package that provides a light-weight frontend to use Apache Spark from R.</li>
</ul>
<h4>Deep learning</h4>
<ul>
<li><a rel="nofollow" href="https://github.com/Microsoft/CNTK-R" target="_blank">CNTK-R</a> – R bindings to the <a rel="nofollow" href="https://docs.microsoft.com/en-us/cognitive-toolkit/" target="_blank">Cognitive Toolkit</a> (CNTK) deep learning library.</li>
<li><a rel="nofollow" href="https://tensorflow.rstudio.com/" target="_blank">tensorflow</a> – R interface to <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-deep-learning-ai-frameworks#tensorflow?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Tensorflow</a>.</li>
<li><a rel="nofollow" href="https://mxnet.incubator.apache.org/api/r/index.html" target="_blank">mxnet</a> – R interface to <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-deep-learning-ai-frameworks#mxnet?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">MXNET</a>, bringing flexible and efficient GPU computing and state-of-art deep learning to R.</li>
<li><a rel="nofollow" href="https://keras.rstudio.com/" target="_blank">keras</a> – R interface to <a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/machine-learning/data-science-virtual-machine/dsvm-deep-learning-ai-frameworks#keras?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Keras</a>.</li>
<li><a rel="nofollow" href="https://github.com/maddin79/darch" target="_blank">darch</a> – Create deep architectures in R.</li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/deepnet/index.html" target="_blank">deepnet</a> – Implement some deep learning architectures and neural network algorithms, including BP, RBM, DBN, Deep autoencoder and so on.</li>
<li><a rel="nofollow" href="https://github.com/cdeterman/gpuR" target="_blank">gpuR</a> – R interface to use GPUs.</li>
</ul>
<h4>Integrations</h4>
<ul>
<li><a rel="nofollow" href="https://docs.microsoft.com/en-us/sql/advanced-analytics/r/revoscaler-overview/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">RevoScaleR</a> – a collection of portable, scalable, and distributable R functions for importing, transforming, and analyzing data at scale, included with Microsoft ML Server.</li>
<li><a rel="nofollow" href="https://docs.microsoft.com/en-us/machine-learning-server/r-reference/microsoftml/microsoftml-package/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">MicrosoftML</a> – a package that provides state-of-the-art fast, scalable machine learning algorithms and transforms for R.</li>
<li><a rel="nofollow" href="https://github.com/h2oai/h2o-3" target="_blank">h2o</a> – R interface to H2O.</li>
</ul>
<h3>Application and service deployment</h3>
<p>R based applications can be easily deployed as service for end-users or developers. The R packages and tools in this category are used for deploying an R-based analytics or applicaiton as services or interfaces that can be conveniently consumed by end-users or developers.</p>
<ul>
<li><a rel="nofollow" href="https://docs.microsoft.com/en-us/machine-learning-server/r-reference/mrsdeploy/mrsdeploy-package?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">mrsdeploy</a> – an R package included with Microsoft ML Server that provides functions for deploying easily-consumable service within R session.</li>
<li><a rel="nofollow" href="https://cran.r-project.org/web/packages/AzureML/index.html" target="_blank">AzureML</a>– an R package to allow one to interact with <a rel="nofollow" href="https://studio.azureml.net/" target="_blank">Azure Machine Learning Studio</a> for publishing R functions as API services.</li>
<li><a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/container-instances/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Azure Container Instances</a> – service to allow running containerized R analytics in Azure.</li>
<li><a rel="nofollow" href="https://docs.microsoft.com/en-us/azure/container-service/?WT.mc_id=Revolutions-blog-davidsmi" target="_blank">Azure Container Service</a> – service that simplifies deployment, management, and operation of orchestrated containers of R analytics in Azure.</li>
<li><a rel="nofollow" href="https://www.rstudio.com/products/shiny/shiny-server/" target="_blank">Shiny server</a> – Develop and publish Shiny based web applications online.</li>
</ul>
<h3>For more information</h3>
<p><a rel="nofollow" href="http://blog.revolutionanalytics.com/2017/07/more-companies-using-r.html" target="_blank">Companies around the world are using R</a> to build enterprise-grade applications on Azure. For in-depth examples (with code and architecture), you can also find a selection of <a rel="nofollow" href="https://github.com/yueguoguo/r-on-azure#real-world-use-cases" target="_blank">R based solutions for real-world use cases</a>. A more detailed list of packages and tools for deploying R in Azure is provided at the link below, and will be updated as new tools become available.</p>
<p>Github (yueguoguo): <a rel="nofollow" href="https://github.com/yueguoguo/r-on-azure" target="_blank">R in Azure</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/2018/01/r-cloud-tools.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 rel="nofollow" href="https://www.r-project.org/" title="The R Project for Statistical Computing">R</a> news and <a rel="nofollow" href="https://www.r-bloggers.com/search/tutorial" title="R tutorials">tutorials</a> on topics such as: <a rel="nofollow" href="https://www.r-bloggers.com/search/data%20science" title="Data science">Data science</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Big%20Data" title="Big Data">Big Data, <a rel="nofollow" href="https://www.r-users.com/" title="R jobs">R jobs</a>, visualization (<a rel="nofollow" href="https://www.r-bloggers.com/search/ggplot2" title="ggplot and ggplot2 tutorials">ggplot2</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/boxplot" title="Boxplots using lattice and ggplot2 tutorials">Boxplots</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/map" title="Maps and gis">maps</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/animation" title="Animation in R">animation</a>), programming (<a rel="nofollow" href="https://www.r-bloggers.com/search/RStudio" title="RStudio IDE for R">RStudio</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/sweave" title="Sweave and literate programming">Sweave</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/LaTeX" title="LaTeX in R">LaTeX</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/SQL" title="SQL and databases">SQL</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/eclipse" title="Eclipse IDE for R">Eclipse</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/git" title="git and github, Version Control System">git</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/hadoop" title="Large data in R using Hadoop">hadoop</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/Web+Scraping" title="Web Scraping of google, facebook, yahoo, twitter and more using R">Web Scraping</a>) statistics (<a rel="nofollow" href="https://www.r-bloggers.com/search/regression" title="Regressions and ANOVA analysis tutorials">regression</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/PCA" title="principal component analysis tutorial">PCA</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/time+series" title="Time series">time series</a>, <a rel="nofollow" href="https://www.r-bloggers.com/search/trading" title="finance trading">trading</a>) and more...
<img width="1" alt="" src="http://feeds.feedburner.com/~r/RBloggers/~4/D31_uuPhXxo" height="1"></a></div>