Getting Started with Python Web Services using Machine Learning Server

Machine Learning Server 9.2 adds support for the full data science lifecycle of your Python-based analytics. In this article, we will go through step by step details of implementing data science lifecycle using Python. These steps include : Creating a VM configured as One-Box [using ARM Templates] Developing python models [using revoscalepy, microsoftml packages in any IDE]…


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…


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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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…


Using R to generate API Client from Swagger

Microsoft R Server’s operationalization feature enables data scientists to deploy and consume web services to operationalize their R analytics. The publishService() function in mrsdeploy package can be used to publish an R code block as a new web service. After it has been deployed, the web service can be: Consumed directly in R by another…


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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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…


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…