Ensuring robustness in ML-enabled software systems requires addressing critical challenges, such as silent failures, out-of-distribution (OOD) data, and adversarial attacks. Traditional software engineering practices, which rely on predefined logic, are insufficient for ML components that depend on data and probabilistic decision-making. To address these challenges, we propose the ML-On-Rails protocol, a unified framework designed to enhance the robustness and trustworthiness of ML-enabled systems in production. This protocol integrates key safeguards such as OOD detection, adversarial attack detection, input validation, and explainability. It also includes a model-to-software communication framework using HTTP status codes to enhance transparency in reporting model outcomes and errors. To align our approach with real-world challenges, we conducted a practitioner survey, which revealed major robustness issues, gaps in current solutions, and highlighted how a standardised protocol such as ML-On-Rails can improve system robustness. Our findings highlight the need for more support and resources for engineers working with ML systems. Finally, we outline future directions for refining the proposed protocol, leveraging insights from the survey and real-world applications to continually enhance its effectiveness.

翻译：确保机器学习赋能软件系统的鲁棒性需要应对若干关键挑战，例如静默故障、分布外数据以及对抗性攻击。传统软件工程实践依赖于预定义逻辑，对于依赖数据和概率决策的机器学习组件而言已显不足。为应对这些挑战，我们提出了ML-On-Rails协议——一个旨在提升生产环境中机器学习赋能系统鲁棒性与可信度的统一框架。该协议整合了多项关键保障机制，包括分布外检测、对抗攻击检测、输入验证及可解释性模块。协议还包含基于HTTP状态码的模型-软件通信框架，以增强模型输出与错误报告的透明度。为使方案更贴合实际挑战，我们开展了从业者问卷调查，揭示了当前系统存在的主要鲁棒性问题与解决方案缺口，并论证了ML-On-Rails等标准化协议如何提升系统鲁棒性。研究结果凸显了为机器学习系统工程师提供更多支持与资源的迫切需求。最后，基于调查见解与真实应用案例，我们规划了完善该协议的后续研究方向，以持续提升其实际效能。