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Azure Machine Learning Now Available as a Function in Azure Stream Analytics

by Dann Anthony Maurno
Assistant Editor, MSDW

By popular demand, Azure customers can now apply Azure Machine Learning (ML) models as a function on top of streaming data to get real-time insights. So wrote Microsoft Senior PM Sudhesh Suresh in a blog post about this preview.

Azure ML is largely a recommendation engine - an enterprise-grade version of, for example, the Netflix recommendation engine that tells you "Because you liked ‘Jaws,' you might like...". Both Dynamics CRM 2016 and Dynamics AX 7 (developed with the latest Azure capabilities) incorporate Azure ML for advanced analytics and decision making.

According to Suresh, numerous Azure customers have asked to combine the real-time analytics capabilities in Azure Stream Analytics with the power of Azure Machine Learning (ML) in quickly building and operationalizing any machine learning model as a web service.

This Azure ML/Azure Stream Analytics capability is now in public preview, and enables you to "score" individual events of streaming data, leveraging an ML model hosted in Azure. This enables you to, for example, build an application for real-time Twitter sentiment analytics. Two more real-world scenarios the Azure ML team is implementing with customers:

  • Using the capability to provide real-time product recommendations on the company website, helping them drive more revenue. Recommendations are served in real time based on website click data, user profile and other contextual information that is scored against an Azure ML product recommendation model.
  • Extracting in real time the topics and sentiment associated with conversations between customers and support staff. Support managers use this information to become aware of any critical customer issues in a timely manner, which in turn improves customer satisfaction and retention.

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About Dann Anthony Maurno

Dann Anthony Maurno is a seasoned business journalist who began his career as International Marketing Manager with Lilly Software, then moved on as a freelancer to write for such prestigious clients as CFO Magazine; Compliance Week;Manufacturing Business Technology; Decision Resources, Inc.; The Economist Intelligence Unit; and corporate clients such as Iron Mountain, Microsoft and SAP. He is the co-author of Thin Air: How Wireless Technology Supports Lean Initiatives(CRC/Productivity Press, 2010).