AWS, Microsoft Partner on Gluon AI Tool

Microsoft and Amazon today announced a new deep learning interface called Gluon.

Supporting both traditional imperative programming and symbolic programming - where computational results are represented in a graph - Gluon is essentially an abstraction layer created to work with deep-learning frameworks Apache MXNet and, soon, Microsoft's Cognitive Toolkit.

The Gluon interface, announced in a joint press release on Thursday, lets developers use a Python API and pre-built neural network components to build machine learning models. "We look forward to our collaboration with Microsoft on continuing to evolve the Gluon interface for developers interested in making machine learning easier to use", said Swami Sivasubramanian, the VP of Amazon AI. That's particularly important for increasingly complex models, since the calculations needed to both train and execute a neural network can take significant amounts of compute power. First, he argued, it's easier to learn and to understand than more formal neural network definition methods. "To make this happen we need to put the right tools in the right hands, and the Gluon interface is a step in this direction".

According to the release, the companies believe that Gluon can be used to create machine learning models for the cloud, mobile apps, and edge device as well. "AWS and Microsoft published Gluon's reference specification so other deep learning engines can be integrated with the interface".

With Gluon, developers use a Python API and pre-built neural network components to build machine learning models, allowing for more experimentation with less complicated code. Speaking of the cloud, it's interesting that AWS and Microsoft-perhaps the top two players in the IaaS market-have partnered on such an endeavor.

Cloud services rivals Amazon Web Services and Microsoft are collaborating on a deep learning library created to accelerate development of models used to construct neural networks. Gluon, which is available as an open source project, will provide a shared set of building blocks that people can use with both Amazon and Microsoft's preferred machine learning frameworks.



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