Edge MLOps: An Automation Framework for AIoT Applications

Emmanuel Raj, David Buffoni, Magnus Westerlund, Kimmo Ahola

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

Abstract

Artificial Intelligence of Things (AIoT) is the combination of artificial intelligence (AI) technologies with the Internet of Things (IoT) infrastructure to achieve more efficient IoT operations and decision making. Edge computing is emerging to enable AIoT applications. In this paper, we develop an Edge MLOps framework for automating Machine Learning at the edge, enabling continuous model training, deployment, delivery and monitoring. To achieve this, we synergize cloud and edge environments. We experimentally validate our framework on a forecasting air quality situation. During validation, the framework showed stability and automatically retrained, integrated, and deployed models for specific environments when their performance deteriorated under a certain threshold.
Original languageEnglish
Title of host publication2021 IEEE International Conference on Cloud Engineering (IC2E)
PublisherIEEE Institute of Electrical and Electronic Engineers
Pages191-200
Number of pages10
ISBN (Electronic)978-1-6654-4970-0
ISBN (Print)978-1-6654-4971-7
DOIs
Publication statusPublished - 8 Oct 2021
MoE publication typeA4 Article in a conference publication
EventIEEE International Conference on Cloud Engineering, IC2E 2021 - San Francisco, CA, USA
Duration: 4 Oct 20218 Oct 2021

Conference

ConferenceIEEE International Conference on Cloud Engineering, IC2E 2021
Period4/10/218/10/21

Keywords

  • Training
  • Cloud computing
  • Automation
  • Atmospheric modeling
  • Computational modeling
  • Pipelines
  • Collaborative work

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