  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.
