An Empirical Study on AI Workflow Automation for Positioning

Abstract: The maturing capabilities of Artificial Intelligence (AI) and Machine Learning (ML) have resulted in increased attention in research and development on adopting AI and ML in 5G and future networks. With the increased maturity, the usage of AI/ML models in production is becoming more widespread, and maintaining these systems is more complex and likely to incur technical debt when compared to standard software. This is due to inheriting all the complexities of traditional software in addition to ML-specific ones. To handle these complexities the field of ML Operations (MLOps) has emerged. The goal of MLOps is to extend DevOps to AI/ML and therefore speed up development and ease maintenance of AI/ML-based software, by, for example, supporting automatic deployment, monitoring, and continuous re-training of models. This thesis investigates how to construct an MLOps workflow by selecting a number of tools and using these to implement a workflow. Additionally, different approaches for triggering re-training are implemented and evaluated, resulting in a comparison of the triggers with regards to execution time, memory and CPU consumption, and the average performance of the Machine learning model.