University of Twente Student Theses
Selecting the Optimal Machine Learning Framework for LiDAR-Based Railroad Inspection and Maintenance at Strukton Rail: A Comprehensive Evaluatio
Uilkema, Tim H. (2023) Selecting the Optimal Machine Learning Framework for LiDAR-Based Railroad Inspection and Maintenance at Strukton Rail: A Comprehensive Evaluatio.
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| Abstract: | The surge in machine learning (ML) applications has made robust ML operational frameworks imperative. Strukton Rail, a railway solutions company, faces significant challenges, including managing extensive LiDAR data, deploying models efficiently on the cloud, maintaining version control, and ensuring user-friendly solutions. In response to these challenges, this research not only evaluates but also provides a structured approach for selecting ML frameworks based on criteria relevant to the needs of Strukton Rail. This contributes to the domain by providing a blueprint for future ML framework selection in similar contexts. The study bridges the gap between ML model experimentation and real-world application, aiming to boost the efficiency of ML applications in practice. Overall, the framework deemed most suitable for Strukton Rail through this process was ClearML. |
| Item Type: | Essay (Bachelor) |
| Clients: | Saxion Hogeschool, Enschede, The Netherlands |
| Faculty: | EEMCS: Electrical Engineering, Mathematics and Computer Science |
| Subject: | 54 computer science |
| Programme: | Business & IT BSc (56066) |
| Link to this item: | https://purl.utwente.nl/essays/96346 |
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