NEW AZURE REFERENCE ARCHITECTURE: Real-time scoring of R machine learning models

First published on MSDN on Dec 13, 2018
We have a new AI Reference Architecture (on the Azure Architecture Center ) from AzureCAT Data Scientist, Hong Ooi . It was edited by Nanette Ray and Mike Wasson . It was reviewed by George Iordanescu (also from AzureCAT AI).

This reference architecture shows how to implement a real-time (synchronous) prediction service in R using Microsoft Machine Learning Server running in Azure Kubernetes Service (AKS).

This architecture is intended to be generic and suited for any predictive model built in R that you want to deploy as a real-time service. Deploy this solution .

The Reference Architecture includes the following information:

  1. Architecture - Explaining the different elements of the architectural diagram.

  2. Performance Considerations - What to watch out for to maintain high levels of performance.

  3. Security Considerations - Network encryption, authentication, authorization, and separating storage.

  4. Monitoring and Logging - We recommend the Kubernetes dashboard and Azure Monitor Insights.

  5. Cost Considerations - How pricing works with Machine Learning Server, licensing alternatives, and managing the compute resources.

  6. Deploy - Our GitHub repo includes prerequisites, setup instructions, deployment and testing steps, as well as the various R files and template.

Head over to the Azure Architecture Center to learn more about this reference architecture, Real-time scoring of R machine learning models .

See Also

Additional related Reference Architectures:

Find all our Reference Architectures here .

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