DEEP-HybidDataCloud provides a comprehensive framework for the development of machine learning, deep learning and artificial intelligence applications for the European Open Science Cloud. We build on top of pan-European e-Infrastructures, following a Service Oriented Architecture, in order to deliver high level and added value services for scientists and the public in general. We provide transparent access to e-Infrastructures both for training and deploying machine learning, deep learning and artificial intelligence models, covering the whole machine learning life cycle. Our framework and reference implementation is based on an approach where we differentiate our users by their knowledge in different areas (domain knowledge, machine learning knowledge, technical knowledge). Therefore we offer them a path where it is possible to perform different tasks, according to their needs and their prior skills. Advanced users will get the more advanced features, and basic users will still obtain enough functionality form the framework. In this regard, the DEEP framework has been proposed as the reference specification for the machine learning, deep learning and data analytics technical specification for the EOSC Workflow management and user interfaces and Data analytics Working Group.
We bring knowledge closer to society by easing the execution of the DEEP marketplace modules. We provide easy methods (i.e. easy to users with no technical knowledge) and instructions to quickly test and use the model’s functionality and exploit its gained knowledge. Moreover, we allow scientists to deploy their applications and services, being possible therefore to build services that exploit the model’s functionality. We are fostering collaboration since all the developed modules, applications and models are directly available for download and reuse. Reproducibility, although not being directly addressed by our project, can be achieved by the publication of a model, application, data and metadata through the portal.
