The Data Daily

Microsoft and Amazon unveil Gluon to help developers adopt machine learning

Microsoft and Amazon unveil Gluon to help developers adopt machine learning

Collaboration in the realm of artificial intelligence leads to some strange bedfellows in the tech world. That’s certainly the case today, when Microsoft and Amazon Web Services announced their collaboration on Gluon, a new programming library for machine learning.

It works by providing a consistent interface for creating machine learning models using a variety of pre-built and highly optimized components. 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.

Gluon’s set of pre-built components are supposed to make it easier for developers to get started building models, and make it faster for machine learning experts to build prototypes of more complex systems that they might want to create by hand, according to a blog post from Matt Wood, AWS’s general manager of AI.

The tools are supposed to take what is usually an inflexible and unwieldy process and make it more approachable and flexible, similar to what programmers are used to with other forms of coding.

Gluon is currently compatible with Apache MXNet, AWS’s preferred machine learning framework, and Microsoft is working to enable its compatibility with its Cognitive Toolkit.

Because Gluon is backed by Microsoft and AWS’s respective deep learning frameworks, it’s also possible for developers to distribute neural network computation from Gluon models across many GPUs for increased speed. 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.

The news comes a few days after another major open source announcement from Redmond in the machine learning realm. Several companies, including Intel, Qualcomm, AMD and ARM, are working with the Open Neural Network Exchange (ONNX) project to help create a shared representation of one popular form of machine learning.

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