One sure way to find the limits of a technology is to have it become popular.

The explosion of interest in machine learning has exposed a long-standing shortcoming: Too much time and effort are spent shuttling data between different applications, and not enough is spent on the actual data processing.Three providers of GPU-powered machine learning and analytics solutions are collaborating to find a way for multiple programs to access the same data on a GPU and process it in-place, without having to transform it, copy it, or execute other performance-killing processes.[ Roundup: TensorFlow, Spark MLlib, Scikit-learn, MXNet, Microsoft Cognitive Toolkit, and Caffe machine learning and deep learning frameworks. | Get a digest of the day’s top tech stories in the InfoWorld Daily newsletter. ]
Data, stay put!
Continuum Analytics, maker of the Anaconda distribution for Python; machine learning/AI specialist; and GPU-powered database creator MapD (now an open source product) have formed a new consortium, called the GPU Open Analytics Initiative (GOAI).To read this article in full or to leave a comment, please click here

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