The development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. The lack of diligence can lead to technical debt, scope creep and misaligned objectives, model misuse and failures, and expensive consequences. Engineering systems, on the other hand, follow well-defined processes and testing standards to streamline development for high-quality, reliable results. The extreme is spacecraft systems, where mission critical measures and robustness are ingrained in the development process. Drawing on experience in both spacecraft engineering and AI/ML (from research through product), we propose a proven systems engineering approach for machine learning development and deployment. Our Technology Readiness Levels for ML (TRL4ML) framework defines a principled process to ensure robust systems while being streamlined for ML research and product, including key distinctions from traditional software engineering. Even more, TRL4ML defines a common language for people across the organization to work collaboratively on ML technologies.

中文翻译：

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机器学习系统的技术准备水平

机器学习系统的开发和部署可以使用现代工具轻松执行，但该过程通常是仓促的，而且是一劳永逸的。缺乏勤奋会导致技术债务、范围蔓延和目标错位、模型误用和失败以及代价高昂的后果。另一方面，工程系统遵循明确定义的流程和测试标准，以简化开发以获得高质量、可靠的结果。极端情况是航天器系统，其关键任务措施和稳健性在开发过程中根深蒂固。借鉴航天器工程和 AI/ML（从研究到产品）的经验，我们为机器学习开发和部署提出了一种经过验证的系统工程方法。我们的 ML 技术准备水平 (TRL4ML) 框架定义了一个原则性流程，以确保系统稳健，同时针对 ML 研究和产品进行精简，包括与传统软件工程的主要区别。更重要的是，TRL4ML 为整个组织的人们定义了一种通用语言，以便在 ML 技术上进行协作。