This post is authored by Wee Hyong Tok, Principal Data Science Manager at Microsoft.
Deep Learning has fueled the emergence of many practical applications and experiences. It has played a central role in making many recent breakthroughs possible, ranging from speech recognition that’s reached human parity in word recognition during conversations, to neural networks that are accelerating the creation of highly precise land cover datasets, to predicting vision impairment, regression rates and eye diseases, among others.
The Microsoft Cognitive Toolkit (CNTK) is the behind-the-scenes magic that makes it possible to train deep neural networks that address a very diverse set of needs, such as in the scenarios above. CNTK lets anyone develop and train their deep learning model at massive scale. Successful deep learning projects also require a few critical infrastructure ingredients – specifically, an infrastructure that:
Enables teams to perform rapid experimentation.
Scales, based on the demands of training.
Handles the increasing load needed to serve a trained model.
Meanwhile, container technologies have been maturing, with more enterprises using containers in their IT environments. Containers are allowing organizations to simplify the development and deployment of applications in various environments (e.g. on-premises, public cloud, hybrid cloud, etc.). Various container orchestration
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