Towards FAIR Workflows for Federated Experimental Sciences

Gayathri Saranathan, Foltin Martin, Aalap Tripathy, Annmary Justine, Maxim Ziatdinov, Ayana Ghosh, Kevin Roccapriore, Supama Bhattacharya, Paolo Faraboschi

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

A de-centralized, peer-to-peer AI metadata framework is demonstrated which can enable end-to-end metadata & lineage tracking for distributed Machine Learning pipelines spanning edge, High Performance Computing, and cloud environments. With a specific example of end-to-end microscopy algorithm and datasets, the proposed method shows how to enable reproducibility, audit trail, provenance of metadata artifacts. The emerging needs of automation in experimental sciences, ML-centric workflows, and FAIR metadata management across federated compute environments is addressed.

Original languageEnglish
Title of host publicationProceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1436-1437
Number of pages2
ISBN (Electronic)9798350354096
DOIs
StatePublished - 2024
Event2nd IEEE Conference on Artificial Intelligence, CAI 2024 - Singapore, Singapore
Duration: Jun 25 2024Jun 27 2024

Publication series

NameProceedings - 2024 IEEE Conference on Artificial Intelligence, CAI 2024

Conference

Conference2nd IEEE Conference on Artificial Intelligence, CAI 2024
Country/TerritorySingapore
CitySingapore
Period06/25/2406/27/24

Keywords

  • AI
  • DKL
  • FAIR
  • HPC
  • ML

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