Microsoft Announces SynapseML for .NET for Large-Scale Machine Learning – Visual Studio Magazine

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Microsoft Announces SynapseML for .NET for Large-Scale Machine Learning

Microsoft announced SynapseML for .NET, building on its open source project for large-scale machine learning that debuted last November.

That open source project in turn builds on Apache Spark and SparkML to simplify the creation of scalable machine learning pipelines while enabling new kinds of machine learning, analytics and model deployment workflows. Formerly called MMLSpark, it contributes many deep learning and data science tools to the Spark ecosystem, such as seamless integration of Spark Machine Learning pipelines with the Open Neural Network Exchange (ONNX), LightGBM, The Cognitive Services, Vowpal Wabbit and OpenCV. Microsoft said those tools enable powerful and highly calable predictive and analytical models for a variety of datasources.

As part of the new SynapseML v0.10 release, Microsoft announced a new set of .NET APIs for massively scalable machine learning.

“This allows you to author, train, and use any SynapseML model from C#, F#, or other languages in the .NET family with our .NET for Apache Spark language bindings,” the company said in an Aug. 9 blog post.


SynapseML in Animated Action
[Click on image for larger, animated GIF view.] SynapseML in Animated Action (source: Microsoft).

The tool can help developers build scalable and intelligent systems across a wide variety of Microsoft domains, including:

“A unified API standardizes many of today’s tools, frameworks, and algorithms, streamlining the distributed ML experience,” Microsoft said last November when it announced the open source project. “This enables developers to quickly compose disparate ML frameworks for use cases that require more than one framework, such as web-supervised learning, search engine creation, and many others. It can also train and evaluate models on single-node, multi-node, and elastically resizable clusters of computers, so developers can scale up their work without wasting resources.”

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David Ramel is an editor and writer for Converge360.

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