Architecture

Service (business)

Operating system

Distributed computing

Software engineering

Machine learning

Visual arts

Economy

Artificial intelligence

Economics

Set (abstract data type)

Programming language

Computer science

Cloud computing

DevOps

IEEE Access

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.

A Cloud-Based Framework for Machine Learning Workloads and Applications