REST Calls using PostMan for R server Operationalization

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…


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…


Reference implementation of credit risk prediction using R

This post is authored by Surendra Tipparaju and Durga Prasad Chappidi at Microsoft Credit risk prediction is one of most common models and yet most revisited. The risk assessment is determined based on dataset and number of features that can be included in the model. We have implemented the initial model few months back in…


Microsoft R Server VMs available in Azure China

 This post is authored by Bharath Sankaranarayan, Principal Program Manager, at Microsoft. We have expanded our footprint of Microsoft R Server Virtual Machines and is available in Azure China, both China North and China East. This will provide our customers the ability to leverage Microsoft R Server 9.0 for building advanced analytics solutions in the…


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…


Exporting large data using Microsoft R (IDE: RTVS)

Introduction Very often in our projects we encounter a need to export huge amount of data (in GBs) and the conventional solution, write.csv, can test anyone’s patience with the time it demands. In this blog, we will learn by doing. We make use of a package that is not very popular, but serves the purpose…


Predicting Hospital Length of Stay (LOS) using SQL Server 2016 with R Services

This post is authored by Bharath Sankaranarayan, Principal Program Manager, at Microsoft. Today we are excited to announce a Hospital length of Stay solution, leveraging SQL Server 2016 with R Services.  This solution accelerator will enable hospitals and healthcare providers to leverage machine learning to improve the prediction on how long a patient is expected to stay….


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…


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…


Microsoft R Server Operationalization Examples

Today, more and more businesses are adopting advanced analytics for mission critical decision making in areas such as fraud detection, healthcare and manufacturing. Typically, the data scientists first build out the predictive models and only then can businesses deploy those models in a production environment and consume them for predictive actions Here are few examples…