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.

The Natural History Collections of Adam Mickiewicz University (AMUNATCOLL) in Poznań contain over 2.2 million specimens. Until recently, access to the collections was limited to specialists and was challenging because of the analogue data files. Therefore, this paper presents a new approach to data sharing called the Scientific, Educational, Public, and Practical Use (SEPP) Model. Since the stakeholder group is broad, the SEPP Model assumes the following key points: full open access to the digitized collections, the structure of metadata in accordance with certain standards, and a versatile tool set for data mining or statistical and spatial analysis. The SEPP Model was implemented in the AMUNATCOLL IT system, which consists of a web portal equipped with a wide set of explorative functionalities tailored to different user groups: scientists, students, officials, and nature enthusiasts. An integral part of the system is a mobile application designed for field surveys, enabling users to conduct studies comparing their own field data and AMUNATCOLL data. The AMUNATCOLL IT database contains digital data on specimens, biological samples, bibliographic sources, and multimedia nature documents. The metadata structure was developed in accordance with ABCD 2.06 and Darwin Core standards.