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Traditional automation technologies alone are not sufficient to enabledriverless operation of trains (called Grade of Automation (GoA) 4) onnon-restricted infrastructure. The required perception tasks are nowadaysrealized using Machine Learning (ML) and thus need to be developed and deployedreliably and efficiently. One important aspect to achieve this is to use anMLOps process for tackling improved reproducibility, traceability,collaboration, and continuous adaptation of a driverless operation to changingconditions. MLOps mixes ML application development and operation (Ops) andenables high frequency software releases and continuous innovation based on thefeedback from operations. In this paper, we outline a safe MLOps process forthe continuous development and safety assurance of ML-based systems in therailway domain. It integrates system engineering, safety assurance, and the MLlife-cycle in a comprehensive workflow. We present the individual stages of theprocess and their interactions. Moreover, we describe relevant challenges toautomate the different stages of the safe MLOps process.

Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain