1 Million predictions/sec with Machine Learning Server web service

Microsoft Machine Learning Server Operationalization allows users to publish their R/Python based models and scripts as “Web-Services” and consume them from a variety of client applications in a scalable, fast, secure and reliable way. In this blog, I want to demonstrate how you can score > 1 Million predictions per second with the ‘Realtime’ web-services. Realtime web services…


Amazon’s AWS Lambda and Alexa Skill with Microsoft Machine Learning Server Operationalization

In my previous blog, I talked about creating Enterprise-friendly Java clients for Machine Learning Server web-services. In this blog, we will see how easily we can extend this Java client to (1) work with  Amazon’s AWS Lambda function and (2) call this lambda function from a Amazon’s Alexa skill.   For this demo, I will assume that you have published…


Host chat-bots with Microsoft Machine Learning Server Operationalization

Microsoft Machine Learning Server Operationalization allows users to create remote R or Python sessions, create Machine Learning models using their favorite R/Python packages and publish them as ‘Web-services’. While web-services are perfect for prediction/scoring scenarios, they are inherently stateless in nature and hence may not be suited for a ‘chat-bot’ like use-case which are often stateful. In this blog,…


Enterprise-friendly Java client for Microsoft Machine Learning Server

Machine Learning Server Operationalization allows users to develop powerful R/Python machine learning models and publish them as ‘web-services’. These web-services then can be consumed by different types of clients. Users can use mrsdeploy R package on their client machines to perform ‘remote execution’  to create remote R sessions on a Server with Machine Learning Server installed and develop…


Consuming O16N Web Services from Azure Functions

Operationalization feature of Microsoft Machine Learning Server allows us to publish R/Python models and code in the form of web services and the consume these services within client applications. This article outlines step-by-step details of consuming the published web service (R language) using Azure Functions (C# TimerTrigger). Azure Functions is a solution for easily running…


Simplifying The Use of Azure Data Science Virtual Machine with R

This post is authored by Le Zhang, Data Scientist, and Graham Williams, Director of Data Science at Microsoft. Azure Data Science Virtual Machine (DSVM) and AzureDSVM package Azure Data Science Virtual Machine (DSVM) is a curated Azure VM image preinstalled and configured with popular tools that are commonly used for data analytics and machine learning,…

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Importing data with Microsoft R Server

In this blog, we would play around a new package RevoScaleR which takes a different approach in handling the data. Though, it works directly with flat files, but, is primarily made for a special kind of file format: XDF which enhances processing of the datasets. RevoScaleR package stores the dataset on disk (hard drive) and…

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Encrypting Communication between Web Node and Compute Node in Linux

This article walks you through the steps for encrypting the traffic between web nodes and compute nodes in Linux using self-signed certificates. If a compute node is inside the web node’s trust boundary, then encryption of this piece isn’t needed. However, if the compute node resides outside of the trust boundary, consider using the compute…


Using Microsoft R Server Operationalization on HDInsight

R Server on HDInsight cluster allows R scripts to use Spark and MapReduce to run distributed computations. You can develop a model and operationalize the model to make predictions by configuring Edge Node as One-Box. We have One-Click Deploy ARM Templates using which you can create R Server on HDInsight Cluster with EdgeNode configured as…


Machine Learning Services in SQL Server 2017

This post is authored by Sumit Kumar, Senior Program Manager at Microsoft. Hopefully, you are already aware of the first release candidate (RC1) of SQL Server 2017 which became available earlier in July. This release includes several powerful enhancements in the Machine Learning Services – the core intelligence engine in SQL Server. This release further…