Linux Foundation launches LF Deep Learning Foundation, open-source Acumos AI Project

March 26, 2018 Brandon Lewis

LOS ANGELES. The Linux Foundation has launched the LF Deep Learning Foundation, a group dedicated to the development of open-source artificial intelligence (AI), machine learning (ML), and deep learning (DL) tools and infrastructure. The first initiative of the LF Deep Learning Foundation is the Acumos AI Project, a federated platform designed to accelerate the development of reusable AI models and workflows. AT&T and Tech Mahindra contributed the initial code for Acumos AI, which is available for download now.

Acumos provides developers and data scientists with a visual workflow for designing AI and ML applications, as well as a marketplace to freely share solutions and data models. The Acumos Marketplace includes components and microservices that allow users to export off-the-shelf AI applications as containers that can be run in public or private cloud environments.

“An open and federated AI platform like the Acumos platform allows developers and companies to take advantage of the latest AI technologies and to more easily share proven models and expertise,” says Jim Zemlin, Executive Director, The Linux Foundation. “Acumos will benefit developers and data scientists across numerous industries and fields, from network and video analytics to content curation, threat prediction, and more.”

The LF Deep Learning Foundation expects further contributions from Baidu and Tencent surrounding their EDL Kubernetes enhancement project and Angel machine learning platform, respectively. The Foundation is also planning to integrate a wide variety of other projects that include toolkits, inferencing engines, and infrastructure deployment architectures.

Founding members of the LF Deep Learning Foundation include Amdocs, AT&T, B.Yond, Baidu, Huawei, Nokia, Tech Mahindra, Tencent, Univa, and ZTE. For more information, visit


About the Author

Brandon Lewis

Brandon Lewis, Editor-in-Chief of Embedded Computing Design, is responsible for guiding the property's content strategy, editorial direction, and engineering community engagement, which includes IoT Design, Automotive Embedded Systems, the Power Page, Industrial AI & Machine Learning, and other publications. As an experienced technical journalist, editor, and reporter with an aptitude for identifying key technologies, products, and market trends in the embedded technology sector, he enjoys covering topics that range from development kits and tools to cyber security and technology business models. Brandon received a BA in English Literature from Arizona State University, where he graduated cum laude. He can be reached by email at

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