From DeployR to R Server Operationalization

If you are currently using DeployR (DeployR Enterprise 7.4 or R Server 8.0.x DeployR), this article will help you understand: The differences between DeployR and R Server 9 Operationalization How to upgrade to R Server 9 Operationalization The differences between DeployR and R Server 9.x Operationalization In R Server 9.0, Microsoft introduced a set of…

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rxExecBy – Productivity and scale with partitioned data

There is often a need to train data for “many small models” instead of a “single big model”. Specifically, users may want to train separate models such as logistic regressions or boosted trees within groups (partitions) like “states”, “countries”, “device id”, etc. or they may want to compute summary statistics such as mean, min, max,…

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Performance: rxExecBy vs gapply on Spark

rxExecBy is a new API of R server release 9.1, it partitions input data source by keys and applies user defined function on each partitions. gapply is a SparkR API that also provides similiar functionality, it groups the SparkDataFrame using specified columns and applies the R function to each group.   Prepare Environment The performance…


rxExecBy Insights on RxSpark Compute Context

rxExecBy is designed to resolve a problem that user has a very big data set, want to partition it into many small partitions, and train models on each partition. This is what we call small data many models. rxExecBy has many features and can run in many different compute contexts, e.g. RxSpark, RxInSqlServer, local. In this blog, I’m going…


Leveraging Microsoft R and in database analytics of SQL Server with R Services through Alteryx Designer

This post is authored by Bharath Sankaranarayan, Principal Program Manager at Microsoft. Being part of the Microsoft R Product team I get to use Microsoft R regularly, but not often with a drag-and-drop editor that simplifies the effort needed to build a solution using machine learning. In this blog, I will take you through a…


Microsoft ML on Spark and Hadoop

MicrosoftML is a new package for Microsoft R Server that adds state-of-the-art algorithms and data transforms to Microsoft R Server functionality. The MicrosoftML package was available in Microsoft R Server for Windows and in SQL Server vNext. Now we bring the power of these algorithms to Spark and Hadoop. Training on a Hadoop/Spark cluster occurs in a…


What’s new in R Server 9.1 Operationalization

In Dec 2016, Microsoft R Server 9.0 introduced a new set of capabilities to help enterprises deploy their R analytics into production environments. In the latest release of R Server 9.1, Microsoft further improves on operationalization capabilities. This article will give a glance on those new exciting capabilities in R Server 9.1. Boost up the…

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Sentiment Analysis with a pre-trained model

Harnessing decades of work on cognitive computing in the context of Bing, Office 365 and Xbox, we are delivering the first installment of pre-trained cognitive models that accelerate time to value in Microsoft R Server 9.1. We now offer a Sentiment Analysis pre-trained cognitive model, using which you can assess the sentiment of an English sentence/paragraph with…

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Microsoft R Server support for Rattle

Rattle – the R Analytical Tool To Learn Easily – is a popular GUI for data mining using R. It presents intuitive graphical interface for data mining and analysis without actually writing the code.  It presents statistical and visual summaries of data, transforms data that can be readily modeled, builds both unsupervised and supervised models…

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Running Pleasingly Parallel workloads using rxExecBy on Spark, SQL, Local and Localpar compute contexts

RevoScaleR function rxExec(), allows you to run arbitrary R functions in a distributed fashion, using available nodes (computers) or available cores (the maximum of which is the sum over all available nodes of the processing cores on each node). The rxExec approach exemplifies the traditional high-performance computing approach: when using rxExec, you largely control how…