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.

中文翻译：

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安全运营

安全保证是大规模部署各种自主系统（例如，自动驾驶汽车）的首要因素。然而，安全工程实践和流程的执行受到现代安全关键系统日益复杂的挑战。对于涉及人工智能 (AI) 和数据驱动技术以及物理世界和数字计算平台的复杂交互的自主系统，此属性变得更加重要。在这份立场文件中，我们强调了将当前安全流程应用于现代自主系统的一些挑战。然后，我们引入了 SafetyOps 的概念——一组实践，它结合了 DevOps、TestOps、DataOps 和 MLOps，以提供高效、连续和可追溯的系统安全生命周期。