{"id":3327,"date":"2023-01-09T08:44:34","date_gmt":"2023-01-09T08:44:34","guid":{"rendered":"https:\/\/www.grycap.upv.es\/?page_id=3327"},"modified":"2023-01-09T08:45:15","modified_gmt":"2023-01-09T08:45:15","slug":"ai4eosc","status":"publish","type":"page","link":"https:\/\/grycap.upv.es\/index.php\/ai4eosc\/","title":{"rendered":"AI4EOSC &#8211; AI for the European Open Science"},"content":{"rendered":"<div class=\"wp-block-image\">\n<figure class=\"aligncenter size-full\"><img loading=\"lazy\" decoding=\"async\" width=\"608\" height=\"132\" src=\"https:\/\/www.grycap.upv.es\/wp-content\/uploads\/2023\/01\/ai4eosc-1.png\" alt=\"\" class=\"wp-image-3325\" srcset=\"https:\/\/grycap.upv.es\/wp-content\/uploads\/2023\/01\/ai4eosc-1.png 608w, https:\/\/grycap.upv.es\/wp-content\/uploads\/2023\/01\/ai4eosc-1-300x65.png 300w\" sizes=\"auto, (max-width: 608px) 100vw, 608px\" \/><\/figure>\n<\/div>\n\n\n<p>The <strong>AI4EOSC<\/strong>&nbsp; (AI for the European Open Science Cloud) project is aiming to deliver an enhanced set of services for the development of <strong>Artificial Intelligence (AI),&nbsp; Machine Learning (ML) and Deep Learning (DL) <\/strong>models and applications. This is to be reached by increasing the service offered in the EU landscape by expanding the <strong>European Open Science Cloud (EOSC) ecosystem<\/strong>.&nbsp;<\/p>\n\n\n\n<p>The services will make use of advanced features such as distributed, federated and split learning; provenance metadata; event-driven data processing services or provisioning of services based on serverless computing.<\/p>\n\n\n\n<p>The project will focus on tools to provide AI, ML DL services by integrating into it <strong>real-life use cases<\/strong> to co-design the project proposal and drive our integration activities. AI4EOSC bases its activities on the technological framework delivered by the DEEP-Hybrid-DataCloud H2020 project. The DEEP platform (provided through the EOSC portal 2) is a production-ready system that is being effectively used by researchers in the EU to train and develop machine learning and deep learning models.&nbsp; A special emphasis is made in ensuring that all the research outputs and sub-products (data, models, metadata, publications, etc.) adhere to the FAIR data and research principles.<\/p>\n\n\n\n<p>The <strong>AI4EOSC<\/strong> <strong>consortium<\/strong> has been assembled to ensure a skills-balanced and complementary set of partners with a strong research, development, technological and innovation background. The consortium gathers several of the most active institutions in the EOSC in terms of development, implementation, deployment and operation of distributed pan-European e-infrastructures, experienced and highly innovative SMEs with a huge potential in the AI field, and a wide experience in technological endeavors. All <strong>partners<\/strong> involved in the project activities have wide experience in software development. As such, several academic partners have developed key components used in the production EU e-Infrastructures.<\/p>\n\n\n\n<p>The consortium is comprised of 10 partners including academic &#8211; the project coordinator CSIC, PSNC, LIP, KIT, UPV, IISAS, INFN, industrial &#8211; Predictia, MicroStep-MIS and WODR. The role of UPV is to:<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li>Coordinate the work package related to architecture co-design and portfolio of services&nbsp;<\/li>\n\n\n\n<li>Lead the tasks related to requirements elicitation for co-design; automated PaaS deployments over multi cloud resources; event-driven serverless approach for scalable AI as a Service solution and, finally, composite AI through serverless function orchestration.&nbsp;<\/li>\n\n\n\n<li>Participate in the starte of the art landscaping and technology scouting and venturing; architecture definition and co-design based on requirements, and software, services and applications quality assurance, among other tasks.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>The AI4EOSC&nbsp; (AI for the European Open Science Cloud) project is aiming to deliver an enhanced set of services for the development of Artificial Intelligence (AI),&nbsp; Machine Learning (ML) and Deep Learning (DL) models and applications. This is to be reached by increasing the service offered in the EU landscape by expanding the European Open [&hellip;]<\/p>\n","protected":false},"author":5,"featured_media":0,"parent":0,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"footnotes":""},"class_list":["post-3327","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/grycap.upv.es\/index.php\/wp-json\/wp\/v2\/pages\/3327","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/grycap.upv.es\/index.php\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/grycap.upv.es\/index.php\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/grycap.upv.es\/index.php\/wp-json\/wp\/v2\/users\/5"}],"replies":[{"embeddable":true,"href":"https:\/\/grycap.upv.es\/index.php\/wp-json\/wp\/v2\/comments?post=3327"}],"version-history":[{"count":2,"href":"https:\/\/grycap.upv.es\/index.php\/wp-json\/wp\/v2\/pages\/3327\/revisions"}],"predecessor-version":[{"id":3330,"href":"https:\/\/grycap.upv.es\/index.php\/wp-json\/wp\/v2\/pages\/3327\/revisions\/3330"}],"wp:attachment":[{"href":"https:\/\/grycap.upv.es\/index.php\/wp-json\/wp\/v2\/media?parent=3327"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}