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):

We will use ARM Template Custom Script Extensions to automate One-Box/Enterprise Configuration.

One-box configuration: As the name suggests, one web node and one compute node run on a single machine. This configuration is useful when you want to explore what it is to operationalize R analytics using R Server. It is perfect for testing, proof-of-concepts, and small-scale prototyping, but might not be appropriate for production usage.

Enterprise configuration: In this configuration, multiple nodes are configured on multiple machines along with other enterprise features like High Availability, Active Directory Authentication, Secure Connectivity etc. This configuration can be scaled up or down by adding or removing nodes.

We have created the following 8 ARM Templates for easy deployment of One-Box and Enterprise Configurations:

  1. OneBox Configuration for Windows
  2. OneBox Configuration for Windows DSVM
  3. OneBox Configuration for Linux
  4. OneBox Configuration for Ubuntu DSVM
  5. Dynamic Scaling Enterprise Configuration on Windows with Azure SQL DB as WebNode Backend and Azure Active Directory Authentication
  6. Dynamic Scaling Enterprise Configuration on Windows DSVM with Azure SQL DB as WebNode Backend and Azure Active Directory Authentication
  7. Dynamic Scaling Enteprise Configuration on Linux with Azure Database for PostgreSQL as WebNode Backend and Azure Active Directory Authentication
  8. Dynamic Scaling Enteprise Configuration on Ubuntu DSVM with Azure Database for PostgreSQL as WebNode Backend and Azure Active Directory Authentication

Just Click “Deploy to Azure” button in the github page to deploy the ARM Templates. Architecture and Instructions to connect are provided in the respective github page.

There are several other ways to deploy ARM Templates: Azure Portal, .NET SDK, Powershell, Azure CLI, Ruby, Visual Studio, REST API, AzureSMR package. Refer to this article for detailed steps.

For any issues (or) feature requests, please use the issues page in github. Code contributions are also welcome by creating a pull request.

Please watch this repo for new templates that might be added in the future : https://github.com/Microsoft/microsoft-r/tree/master/mlserver-arm-templates

USEFUL LINKS: 

Introducing Microsoft Machine Learning Server 9.2 Release
What's new in Machine Learning Server
Operationalize analytics with Machine Learning Server
Configuring Machine Learning Server to operationalize analytics (One-Box Configuration)
Configuring Machine Learning Server to operationalize analytics (Enterprise Configuration)
Set up an auto-scale environment to operationalize your R analytics, with just ONE CLICK

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