As ML (Machine Learning) and deep neural networks get more used in many systems,the need to understand and test such systems becomes more actual. When designing a newsystem that contains ML models, the safety of this system becomes inevitably important.This rises the need to discuss a strategy to deal with the potential problems and weak-nesses in such a system. This thesis provides findings from literature and illustrates thepotential strategies in the area of image recognition in a comprehensive way. Lastly, theresult presented in this thesis shows that using an ML component in a complex softwaresystem with high safety requirements requires adopting software methodologies, such asMLOps (Machine learning operations) to monitor such a system and give suggestions tohow to test and verify an ML model integrated into a larger software system.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:liu-197799 |
| Date | January 2023 |
| Creators | Hanash, Ahmad |
| Publisher | Linköpings universitet, Institutionen för datavetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | English |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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