Abstract
Automated Driving Systems (ADS) will operate in an open world. Since it is difficult to specify all possible situations in this open world a priori and the world will most likely change during the system’s lifecycle, it requires agile MLOps cycles, including testing & validation in the field. In this paper, we present how the safe MLOps process proposed in Zeller et al. [25] is realized in the safe.trAIn project using Git-centric methods. Thereby, appropriate tooling support is provided at the different stages of the safe MLOps process to enable continuous development and safety assurance of ML-based systems in the railway domain.
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This research received funding from the Federal Ministry for Economic Affairs and Climate Action (BMWK) and the European Union under grant agreements 19I21039A.
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Zeller, M. et al. (2024). Continuous Development and Safety Assurance Pipeline for ML-Based Systems in the Railway Domain. In: Ceccarelli, A., Trapp, M., Bondavalli, A., Schoitsch, E., Gallina, B., Bitsch, F. (eds) Computer Safety, Reliability, and Security. SAFECOMP 2024 Workshops. SAFECOMP 2024. Lecture Notes in Computer Science, vol 14989. Springer, Cham. https://doi.org/10.1007/978-3-031-68738-9_36
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