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
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.
