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Amalur: Data Integration Meets Machine Learning
[article]
2023
arXiv
pre-print
The data needed for machine learning (ML) model training, can reside in different separate sites often termed data silos. For data-intensive ML applications, data silos pose a major challenge: the integration and transformation of data demand a lot of manual work and computational resources. With data privacy and security constraints, data often cannot leave the local sites, and a model has to be trained in a decentralized manner. In this work, we present a vision on how to bridge the
arXiv:2205.09681v3
fatcat:nmz2dyjy3zhvjd6xw7a6rius7a