AWS, Microsoft launch deep learning library


Jeff Bigham, associate professor at Carnegie Mellon's Human-Computer Interaction Institute, suggested both Amazon and Microsoft are thinking more in terms of business opportunities than research advancement.

Gluon allows developers to build machine learning models with a simple Python scripting language API, along with pre-built and optimised neural network components. It's then implemented using code structures similar to the ones used in app and website development.

Gluon provides an interface, in the form of an API, for building neural networks, something that's not all that easy to do for those not steeped in the art. And because of the complexity of each component, the process can take a long time.

AI development, "especially deep learning models, isn't easy", added Microsoft's Eric Boyd. However, these tend to complicate the code that defines how the model operates. Additionally, it will allow them to debug and update neural networks much more smoothly. Third, such dynamism means the neural network model and its training algorithm can inform one another during the definition process and the training process. The end product is a powerful deep learning interface that's both accessible and performant. The Gluon interface will provide developers the ability to experiment with various neural network models, along with a training method which has barely any impact upon the performance of the underlying engine. Currently, the deep learning library only works with Apache MXNet, Microsoft has noted that support for Microsoft Cognitive Toolkit (CNTK) will be available soon. Support for Microsoft's Cognitive Toolkit will be added in a future release.

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"Today's reality is that building and training machine learning models requires a great deal of heavy lifting and specialised expertise", Sivasubramanian said.

"This is why Microsoft has collaborated with AWS to create the Gluon interface and enable an open AI ecosystem where developers have freedom of choice".

Gluon was developed by the two as an open source project and is aimed at prototyping, building, training and deploying machine learning models for the cloud, devices at the edge and mobile apps, the companies said. Gluon also won't affect the training performance at all, as you'll be able to train your models at the same speed you'd get when using engines like Apache MXNet directly.