Cloud

Microsoft reveals Azure IoT Edge: Putting AI at the furthest reaches of your network

Redmond revealed a variety of new services to help firms take advantage of AI, ranging from new analytics tools to easier ways to incorporate machine learning into software.

Machine-learning systems promise to help firms automatically spot useful information and take intelligent decisions based on the mass of data they collect.

At Microsoft's Connect(); 2017 conference today, Redmond revealed a variety of new services to help firms take advantage of AI services, ranging from new analytics tools to easier ways to incorporate machine learning into software.

Azure IoT Edge, released in preview today, is designed to help businesses generate useful insights from the rapidly growing amount of data collected by simple sensors and computers situated at the edge of networks — without having to send that data for central processing.

"In our customers' worlds, devices and data are often locked in remote places, like oil wells and farms, or in mission-critical places like hospitals and factories," said Microsoft corporate VP of communications Frank Shaw, speaking ahead of event.

SEE: Research: How big data is driving business insights in 2017 (Tech Pro Research)

"Where connectivity can be expensive or unreliable, having IoT devices that can do local processing outside of the cloud is a big advantage under these conditions," he said, giving the example of rapid anomaly detection by remote devices.

"We're delivering a new set of breakthrough cloud capabilities, for deploying cloud intelligence and IoT devices with Azure Machine Learning, Azure Functions and Azure Stream Analytics," said Shaw, adding the service "simplifies the deployment and management of workflows at the edge" by providing tools for cleaning, aggregating and analyzing data locally.

Azure IoT Edge lets developers build and test container-based modules using C, Java, C#, Node.js and Python .

In a separate announcement, Microsoft's Visual Studio IDE has been updated to add tools that make it simpler to develop machine-learning models on "any framework or language" and embed those models into applications. Meanwhile, updates to .NET should also make it easier for .NET developers to incorporate AI models into their software, according to Shaw.

Finally, Azure SQL Database Machine Learning services will also be available in preview. This new service promises to make it easier to use R-based machine-learning models inside Azure SQL Database, with Shaw saying it offers a "seamless" process for developing and training models in Azure Machine Learning and then deploying those models directly to Azure SQL Database. Developers will also be able to use the service to run T-SQL queries, to enable faster predictions in intelligent apps.

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About Nick Heath

Nick Heath is chief reporter for TechRepublic. He writes about the technology that IT decision makers need to know about, and the latest happenings in the European tech scene.

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