University of Twente Student Theses
AI in the Wild : Robust evaluation and optimized fine-tuning of machine learning algorithms deployed on the edge
Burgt, A.P. van der (2023) AI in the Wild : Robust evaluation and optimized fine-tuning of machine learning algorithms deployed on the edge.
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| Abstract: | In research on applying Machine learning to Embedded systems in the field, little research has been done in the evaluation of deployed devices. Often a system is trained with lots of data, hoping to encapsulate everything that a deployed system may encounter. Next to that, real-life data encapsulates the need to research data which has not been seen yet and thus research the influence of out-of-distribution data. In this research, attention will be brought to the evaluation of deployed edge devices and the trade-off between evaluation reliability and power consumption. Furthermore, the trade-off between using cloud hardware and edge hardware is discussed, where it finally culminates in the trade-off between fine-tuning on the edge versus fine-tuning in the cloud. These questions should give insight into the evaluation of a deployed system using edge intelligence, as well as how best to train and further fine-tune these systems |
| Item Type: | Essay (Master) |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 53 electrotechnology, 54 computer science |
| Programme: | Embedded Systems MSc (60331) |
| Link to this item: | https://purl.utwente.nl/essays/95066 |
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