Taming Data Quality in AI-Enabled Industrial Internet of Things
Peer reviewed, Journal article
Accepted version
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Date
2022Metadata
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Abstract
We address the problem of taming data quality in artificial intelligence (AI)-enabled Industrial Internet of Things systems by devising machine learning pipelines as part of a decentralized edge-to-cloud architecture. We present the design and deployment of our approach from an AI engineering perspective using two industrial case studies.