RI-SCALE_D3.1 – AI Systems and Models Specification and Roadmap

The Data Exploitation Platform (DEP) enables Research Infrastructures (RIs) to scale their AI applications across large-scale computing infrastructure, such as on cloud and High Performance Computing (HPC) systems, and enables scientists to train and/or run AI models at scale with RI scientific data. Work Package (WP) 3 plays a central role in enabling these capabilities by providing the technical solutions needed to integrate AI functionalities in the DEP. This deliverable outlines the technical specifications of the AI solutions proposed in WP3 for the DEP, detailing their main features, planned developments and integrations. The document also presents user stories that illustrate scenarios of accessing the DEP for different kinds of users. These stories highlight the DEP access mechanisms and the role of the software solutions in the DEP. The AI applications and their compute and data requirements are also defined in this document. These requirements and the user stories guide the modular architecture of WP3, which enables flexible and customizable definition of workflows for DEP users. Finally, the document proposes the creation of testbeds, which form the methodological basis for realizing the technical implementations in the DEP.

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Publication Details

Journal
Zenodo (CERN European Organization for Nuclear Research)
Published
2025-08-29
DOI
https://doi.org/10.5281/zenodo.16993121
Primary Topic
Geochemistry and Geologic Mapping
Type
article
Field-Weighted Citation Impact
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article

RI-SCALE_D3.1 – AI Systems and Models Specification and Roadmap

Zenodo (CERN European Organization for Nuclear Research)
Geochemistry and Geologic Mapping
article

RI-SCALE_D3.1 – AI Systems and Models Specification and Roadmap

article en

Abstract

The Data Exploitation Platform (DEP) enables Research Infrastructures (RIs) to scale their AI applications across large-scale computing infrastructure, such as on cloud and High Performance Computing (HPC) systems, and enables scientists to train and/or run AI models at scale with RI scientific data. Work Package (WP) 3 plays a central role in enabling these capabilities by providing the technical solutions needed to integrate AI functionalities in the DEP. This deliverable outlines the technical specifications of the AI solutions proposed in WP3 for the DEP, detailing their main features, planned developments and integrations. The document also presents user stories that illustrate scenarios of accessing the DEP for different kinds of users. These stories highlight the DEP access mechanisms and the role of the software solutions in the DEP. The AI applications and their compute and data requirements are also defined in this document. These requirements and the user stories guide the modular architecture of WP3, which enables flexible and customizable definition of workflows for DEP users. Finally, the document proposes the creation of testbeds, which form the methodological basis for realizing the technical implementations in the DEP.

Zenodo (CERN European Organization for Nuclear Research)
Medpace (United States) (US), European Bioinformatics Institute (GB), University of Helsinki (FI), Forschungszentrum Jülich (DE), University of Trento (IT), Türkiye Bilimsel ve Teknolojik Araştırma Kurumu (TR), Medical University of Graz (AT), EGI (NL), European Incoherent Scatter Scientific Association (SE), CMCC Foundation - Euro-Mediterranean Center on Climate Change (IT), Hyperion Technologies (Canada) (CA), Istituto Nazionale di Fisica Nucleare, Sezione di Perugia (IT), KTH Royal Institute of Technology (SE)
Industry, innovation and infrastructure
Openalex Percentile: Top 59%
Geochemistry and Geologic Mapping
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