Aims to develop the new generation of e-infrastructures that harness the latest generation technologies supporting deep learning and other intensive computing techniques to exploit very large data sources.

The project provided the corresponding services to lower the adoption barriers for new communities and users, satisfying the needs of both research, education communities, and citizen science.

The collection of Use Cases in the DEEP-HybridDataCloud project have been chosen from several communities and different scientific disciplines:

• Lattice QCD for massive data analysis: Quantum Chromodynamics (QCD) is the theory that describes the interaction responsible for the confinement of quarks inside hadrons, the so-called strong interaction. The amount of data that needs to be analyzed in a medium-size project is on the order of the Terabyte. The purpose of this Use Case is to serve as a pilot for designing such a data configuration tool to have general applicability for similar usage scenarios in other scientific areas.
• Plant classification with Deep Learning: Collaborative citizen science platforms have sprung enabling users to easily share easily their observations. We intend to collect those freely available observations to build Deep Learning tools around them. This Use Case describes a tool to automatically identify plant species from images using Deep Learning. This can be very helpful to automatically monitor biodiversity at a large-scale and therefore relieving scientists from the tedious task of having to hand-label images. In addition, this tool can be easily retrained to perform image classification on different datasets by a user without expert (Machine Learning) knowledge.
• Deep Learning application for monitoring through satellite imagery: The applications of the Machine Learning techniques range from remote object detection, terrain segmentation to meteorological prediction. Our use case will implement one of these applications to demonstrate the potential of combining satellite imagery and Machine Learning techniques in a Cloud Infrastructure.