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A Cloud-Based Framework for Machine Learning Workloads and Applications
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DOI：10.1109/ACCESS.2020.2964386.png)
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Abstract
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In this paper we propose a distributed architecture to provide machine learning practitioners with a set of tools and cloud services that cover the whole machine learning development cycle: ranging from the models creation, training, validation and testing to the models serving as a service, sharing and publication. In such respect, the DEEP-Hybrid-DataCloud framework allows transparent access to existing e-Infrastructures, effectively exploiting distributed resources for the most compute-intensive tasks coming from the machine learning development cycle. Moreover, it provides scientists with a set of Cloud-oriented services to make their models publicly available, by adopting a serverless architecture and a DevOps approach, allowing an easy share, publish and deploy of the developed models.
Keywords:
Cloud computing
computers and information processing
deep learning
distributed computing
machine learning
serverless architectures
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