Feature Engineering using R

Feature Engineering is paramount in building a good predictive model. It’s significant to obtain a deep understanding of the data that is being used for analysis. The characteristics of the selected features are definitive of a good training model. Why is Feature Engineering important? Too many features or redundant features could increase the run time complexity of…


11 ways to deploy R Server on HDInsight Cluster

In this article, we will discuss 11 possible ways to deploy R Server on HDInsight Cluster. Some of these ways will help in automating the cluster creation (using scripts). Majority of them are related to deployment using Azure Resource Manager Templates. ARM Templates are very useful and can be deployed in several ways. Here are the…


Getting started with GPU acceleration for MicrosoftML’s rxNeuralNet

MicrosoftML‘s rxNeuralNet model supports GPU acceleration. To enable GPU acceleration, you need to do a few things:   Install and configure CUDA on your Windows machine. For the purpose of this setup and later performance comparison, this is the machine used in this blog. There is an excellent old blog on how to do this. However, it’s slightly out of date…

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Image Segmentation Using MicrosoftML

In computer vision, the goal of image segmentation is to cluster pixels into salient image regions, i.e., regions corresponding to individual surfaces, objects or natural parts of objects. The goal of segmentation is simplify and/or change the representation of an image into something that is more meaningful and easier to analyze. More precisely, image segmentation is…

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mrsdeploy R package – ServiceOption data-dir

The mrsdeploy package 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. When you publish a service there two input parameters code and model which can come from files path that can be .R script file…


REST Calls using PostMan for R server O16N

The Microsoft R Server operationalization REST APIs are exposed by R Server’s operationalization server, a standards-based server technology capable of scaling to meet the needs of enterprise-grade deployments. With the operationalization feature configured, the full statistics, analytics and visualization capabilities of R can now be directly leveraged inside Web, desktop and mobile applications. Core Operationalization…


Remote Spark Compute Context using PuTTY on Windows

If you are running Microsoft R Server/Microsoft R Client from a Windows computer equipped with PuTTY, you can create a compute context that will run RevoScaleR functions from your local client in a distributed fashion on your Hadoop cluster. You use RxSpark to create the compute context, but use additional arguments to specify your user…


Classify Yelp restaurant reviews’ food origin with MicrosoftML

Yelp restaurant reviews are one of the most useful resources people use to pick restaurants. Reviews themselves not only carry sentiment towards the dining experience but also contain “meta-information” about the restaurant. For example, looking at a review that says We can tell that this is a Japanese restaurant since it mentions omakase and sushi. Natural language processing and machine…

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Microsoft R Server – Using Hive data source in Spark compute context

Before Microsoft R Server 9.0 release, if you needed to perform analytics on your Hive or Parquet data you had to first manually export to some supported format (e.g., csv) and then use something like RxTextData to perform analytics after potentially uploading the text data to HDFS. With Microsoft R Server 9.0 release, Spark compute…

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