Configuring Microsoft Machine Learning Server to Operationalize Analytics using ARM Templates

To benefit from Machine Learning Server’s web service deployment and remote execution features, you must first configure the server after installation to act as a deployment server and host analytic web services. The installation process is already taken care by using these Azure Marketplace Images (which come with Machine Learning Server pre-installed): Data Science Virtual…


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…


Pre-trained AlexNet model for MicrosoftML’s rxNeuralNet

The rxNeuralNet model in MicrosoftML package supports custom neural networks defined using the NET# language. We can use the NET# language to define a convolutional neural network. In this blog we will give a NET# definition string for the AlexNet model. The model is a direct conversion of the Caffe implementation. It’s worth noting that an R implementation of AlexNet is barely available at the time this…


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…


Galaxy classification with neural networks: a data science workflow

Recently at the Microsoft Ignite 2017 conference on the Gold Coast, I gave a talk about some cool new features we’ve introduced in Microsoft R Server 9 in the last 12 months: MicrosoftML, a powerful package for machine learning Easy deployment of models using SQL Server R Services Creating web service APIs with R Server Operationalisation (previously…


Best practices for executing embarrassingly parallel workloads with R Server on Spark

Introduction An embarrassingly parallel workload or problem is one where little or no effort is needed to separate the problem into a number of parallel tasks. This is often the case where there is little or no dependency or need for communication between those parallel tasks, or for results between them.   In this blog,…


SQL R Services optimization for concurrent execution of sp_execute_external_script

With SQL Server 2016, we have introduced in-database analytics by bringing R closer to the database. This allows the compute to happen closer to the data,  and also leverage the power of SQL Server including resource governance. For production scenario, our guideline includes embedding ‘R’ scripts inside sp_execute_external_script (SPEES), which internally spawns processes for R…


Microsoft R Server 9.0 VMs now support Ubuntu and Korea regions

I am pleased to announce that the Microsoft R Server 9.0 are now available on all Azure regions and in this release we have refreshed the R Server Only SQL Server 2016 Enterprise Edition, R Server on Linux (CentOS) and added R Server on Linux (Ubuntu).  With this release we have brought a lot of…