AI4EOSC: a Federated Cloud Platform for Artificial Intelligence in Scientific Research
cs.DC
/ Authors
Ignacio Heredia, Álvaro López García, Fernando Aguilar Gómez, Diego Aguirre, Caterina Alarcón Marín, Khadijeh Alibabaei, Lisana Berberi, Miguel Caballer, Amanda Calatrava, Pedro Castro
and 21 more authors
Alessandro Costantini, Mario David, Jaime Díez Stefan Dlugolinsky, Borja Esteban Sanchis, Giacinto Donvito, Leonhard Duda, Saúl Fernandez, Andrés Heredia Canales, Valentin Kozlov, Sergio Langarita, João Machado, Germán Moltó, Daniel San Martín, Martin Šeleng, Giang Nguyen, Marcin Płóciennik, Marta Obregón Ruiz, Susana Rebolledo Ruiz, Vicente Rodriguez, Judith Sáinz-Pardo Díaz, Viet Tran
/ 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.