AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research

• URL: http://arxiv.org/abs/2512.16455v1
• Date: Thu, 18 Dec 2025 12:20:31 GMT
• Title: AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research
• Authors: Ignacio Heredia, Álvaro López García, Germán Moltó, Amanda Calatrava, Valentin Kozlov, Alessandro Costantini, Viet Tran, Mario David, Daniel San Martín, Marcin Płóciennik, Marta Obregón Ruiz, Saúl Fernandez, Judith Sáinz-Pardo Díaz, Miguel Caballer, Caterina Alarcón Marín, Stefan Dlugolinsky, Martin Šeleng, Lisana Berberi, Khadijeh Alibabaei, Borja Esteban Sanchis, Pedro Castro, Giacinto Donvito, Diego Aguirre, Sergio Langarita, Vicente Rodriguez, Leonhard Duda, Andrés Heredia Canales, Susana Rebolledo Ruiz, João Machado, Giang Nguyen, Fernando Aguilar Gómez, Jaime Díez,
• Abstract summary: We describe a federated compute platform dedicated to support Artificial Intelligence in scientific workloads.<n>It delivers consistent, transparent access to a federation of physically distributed e-Infrastructures.<n>The platform is able to offer an integrated user experience covering the full Machine Learning lifecycle.
• Score: 44.97138432029079
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we describe a federated compute platform dedicated to support Artificial Intelligence in scientific workloads. Putting the effort into reproducible deployments, it delivers consistent, transparent access to a federation of physically distributed e-Infrastructures. Through a comprehensive service catalogue, the platform is able to offer an integrated user experience covering the full Machine Learning lifecycle, including model development (with dedicated interactive development environments), training (with GPU resources, annotation tools, experiment tracking, and federated learning support) and deployment (covering a wide range of deployment options all along the Cloud Continuum). The platform also provides tools for traceability and reproducibility of AI models, integrates with different Artificial Intelligence model providers, datasets and storage resources, allowing users to interact with the broader Machine Learning ecosystem. Finally, it is easily customizable to lower the adoption barrier by external communities.

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