Deep Learning Platform launched by Linux Foundation

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The Linux Foundation is supporting multiple open source foundations and projects like Cloud Foundry, the Cloud Native Computing Foundation, CNI, CodeAurora, IOTivity and many more. They have added another feather to their cap by flagging off LF Deep Learning Foundation  on March, 2018. This foundation supports open source inventions in artificial intelligence, machine learning and deep learning. Also, it paves a path for these valuable technologies to be easily accessible to data scientists and developers all around the world.

LF Deep Learning Foundation was started by Amdocs, B.Yond, AT&T, Baidu, Huwaei, Nokia, Tech Mahindra, Tencent, Univa and ZTE, who are enabling the developers and users of the tool with a neutral space where they will be able to collaborate their efforts for speedy and widespread adoption of deep learning technologies.

This will offer a platform where deep learning can carry long term strategies as well as support various projects based on AI, machine learning and deep learning ecosystems. Acumos AI project launched by LF Deep Learning will provide an all-inclusive platform for AI model discovery, development and sharing. The neutral space provided by LF Deep Learning will enable the open source community to support entire ecosystems of projects.

The Acumos AI project is providing a platform where it is easy to create, share, discover and apply machine learning, deep learning and analytics models eventually making the potential of AI accessible to the users. AT&T and Tech Mahindra has provided the initial code for Acumos AI project.

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Soon LF Deep Learning Foundation will be privileged with projects from Baidu and Tencent. The Baidu’s EDL project equips Kubernetes with the feature of elastic scheduling and uses PaddlePaddle’s fault tolerant property to specifically improve the complete usage of Kubernetes clusters. For big data/models, Tencent’s Angel project provides distributed high performing machine learning platform developed in collaboration with Peking University. A billion parameters can be supported by this project.

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