{"id":1262,"date":"2024-11-15T14:19:13","date_gmt":"2024-11-15T22:19:13","guid":{"rendered":"https:\/\/mlcommons.org\/?page_id=1262"},"modified":"2024-12-04T07:57:29","modified_gmt":"2024-12-04T15:57:29","slug":"mlcube-project","status":"publish","type":"page","link":"https:\/\/mlcommons.org\/mlcube-project\/","title":{"rendered":"MLCube Project"},"content":{"rendered":"\n
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MLCube Project<\/h1>\n\n\n\n

The use and impact of machine learning is not only limited by technical capabilities, but also by the operational processes to develop, share, and deploy ML models. The industry needs simple and interchangeable building blocks that can be easily shared for experimentation then later composed into mature and robust workflows.<\/p>\n\n <\/div>\n <\/div>\n <\/div>\n\n <\/div>\n<\/section>\n\n\n\n

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How It Works<\/h2>\n\n\n\n

MLCube\u00ae is a set of common conventions for creating ML software that can \u201cplug-and-play\u201d on many different systems. MLCube makes it easier for researchers to share innovative ML models, for a developer to experiment with many different models, and for software companies to create infrastructure for models. It creates opportunities by putting ML in the hands of more people.<\/p>\n\n\n\n

MLCube isn\u2019t a new framework or service; MLCube is a consistent interface to machine learning models in containers like Docker. Models published with the MLCube interface can be run on local machines, on a variety of major clouds, or in Kubernetes clusters \u2013 all using the same code. MLCommons\u00ae provides simple open source \u201crunners\u201d for each of these environments that make training a model in an MLCube a single command, but MLCube is also designed to make it easy to build new infrastructure based on the interface.<\/p>\n<\/div>\n\n\n\n

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