Edge Model Deployment is the process of operationalizing machine learning models on edge devices by optimizing, distributing, and executing inference workflows at or near the data source, distinguishing it from centralized cloud-based deployment through its emphasis on low latency, bandwidth efficiency, and context-aware adaptability.
6
Publications
534
Citations
16
Authors
8
Institutions
Publications per year
2017–2026
6
Parents
Publications
6
Edge MLOps: An Automation Framework for AIoT Applications
Emmanuel Raj, David Buffoni, Magnus Westerlund et al. · 2021 · 50 citations
Operationalizing machine learning models
Ask Berstad Kolltveit, Jingyue Li · 2022 · 31 citations · Full text
1–5 of 6
Authors
16
- XP
1
h-index
- MJ
1
h-index
- ER
1
h-index
- GM
1
h-index
- LM
1
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1–5 of 16
Institutions
8
University College Dublin
Dublin, Ireland
1 h-index
2 publications
348 citations
Origin Enterprises (Ireland)
Dublin, Ireland
1 h-index
1 publications
174 citations
VTT Technical Research Centre of Finland
Espoo, Finland
1 h-index
1 publications
50 citations
Ritsumeikan University
Kyoto, Japan
1 h-index
3 publications
765 citations
Arcada University of Applied Sciences
Helsinki, Finland
1 h-index
1 publications
50 citations
Norwegian University of Science and Technology
Trondheim, Norway
1 h-index
2 publications
62 citations
Tieto (Finland)
Helsinki, Finland
1 h-index
1 publications
50 citations
Fudan University
Shanghai, China
1 h-index
4 publications
52 citations
1–8 of 8
Venues
- Artificial Intelligence in Agriculture
1 publications · 174 citations
1
h-index
- Computers
1 publications · 255 citations
1
h-index
- Annual Computer Security Applications Conference
Conference · 1 publications · 13 citations
1
h-index
- arXiv (Cornell University)
Repository · 1 publications · 11 citations
1
h-index
- Applied Sciences
1 publications · 11 citations
1–5