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Microsoft Open Sources DMTK, A Distributed Machine Learning Library

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Joining a list of major tech giants, Microsoft has today announced the open-sourcing of a new distributed machine learning library.

The toolkit, available now on GitHub, is designed for distributed machine learning — using multiple computers in parallel to solve a complex problem. It contains a parameter server-based programming framework, which makes machine learning tasks on big data highly scalable, efficient and flexible.

It also contains two distributed machine learning algorithms, which can be used to train the fastest and largest topic model and the largest word-embedding model in the world. The toolkit offers rich and easy-to-use APIs to reduce the barrier of distributed machine learning, so researchers and developers can focus on core machine learning tasks like data, model and training.

The toolkit is unique because its features transcend system innovations by also offering machine learning advances, the researchers said. With the toolkit, the researchers said developers can tackle big-data, big-model machine learning problems much faster and with smaller clusters of computers than previously required.

Microsoft is not alone though, in open-sourcing deep-learning and machine learning tools. Google too launched TenserFlow, the second iteration of its deep learning predecessor DistBelief.

Editor-at-large and co-founder at The Tech Portal. He is a tech enthusiast with interests in new-age technology fields like Ai, Machine Learning, AR/VR, Outer Space and related stuff. Drop him a mail anytime, very reachable.

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