Towards a safe MLOps Process for the Continuous Development and Safety Assurance of ML-based Systems in the Railway Domain
This addresses the need for reliable and efficient ML development in the safety-critical railway domain, though it appears incremental as it adapts existing MLOps concepts to this specific context.
The paper tackles the challenge of developing and deploying machine learning (ML) systems for driverless train operation by proposing a safe MLOps process that integrates system engineering, safety assurance, and the ML lifecycle to improve reproducibility, traceability, and continuous adaptation.
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.