SafetyOps
This addresses safety assurance for industries deploying AI-driven autonomous systems, but it is a conceptual position paper without implementation results.
The paper tackles the challenge of applying traditional safety engineering to complex autonomous systems by proposing SafetyOps, a set of practices combining DevOps, TestOps, DataOps, and MLOps to enable an efficient, continuous, and traceable safety lifecycle.
Safety assurance is a paramount factor in the large-scale deployment of various autonomous systems (e.g., self-driving vehicles). However, the execution of safety engineering practices and processes have been challenged by an increasing complexity of modern safety-critical systems. This attribute has become more critical for autonomous systems that involve artificial intelligence (AI) and data-driven techniques along with the complex interactions of the physical world and digital computing platforms. In this position paper, we highlight some challenges of applying current safety processes to modern autonomous systems. Then, we introduce the concept of SafetyOps - a set of practices, which combines DevOps, TestOps, DataOps, and MLOps to provide an efficient, continuous and traceable system safety lifecycle. We believe that SafetyOps can play a significant role in scalable integration and adaptation of safety engineering into various industries relying on AI and data.