Workflows for bringing data science on the cloud/edge computing continuum

Research infrastructures play a crucial role in the development of data science. In fact, the conjunction of data, infrastructures and analytical methods enable multidisciplinary scientists and innovators to extract knowledge and to make the knowledge and experiments reusable by the scientific community, innovators providing an impact on science and society. Resources such as data and methods, help domain and data scientists to transform research in an innovation question into a responsible datadriven analytical process. On the other hand, Edge computing is a new computing paradigm that is spreading and developing at an incredible pace. Edge computing is based on the assumption that for certain applications is beneficial to bring the computation as closer as possible to data or end-users. This paper discusses about this topic by describing an approach for writing data science workflows targeting research infrastructures that encompass resources located at the edge of the network.