  Traditional automation technologies alone are not sufficient to enable
driverless operation of trains (called Grade of Automation (GoA) 4) on
non-restricted infrastructure. The required perception tasks are nowadays
realized using Machine Learning (ML) and thus need to be developed and deployed
reliably and efficiently. One important aspect to achieve this is to use an
MLOps process for tackling improved reproducibility, traceability,
collaboration, and continuous adaptation of a driverless operation to changing
conditions. MLOps mixes ML application development and operation (Ops) and
enables high frequency software releases and continuous innovation based on the
feedback from operations. In this paper, we outline a safe MLOps process for
the continuous development and safety assurance of ML-based systems in the
railway domain. It integrates system engineering, safety assurance, and the ML
life-cycle in a comprehensive workflow. We present the individual stages of the
process and their interactions. Moreover, we describe relevant challenges to
automate the different stages of the safe MLOps process.
