Machine learning (ML) components are being added to more and more critical and impactful software systems, but the software development process of real-world production systems from prototyped ML models remains challenging with additional complexity and interdisciplinary collaboration challenges. This poses difficulties in using traditional software lifecycle models such as waterfall, spiral or agile model when building ML-enabled systems. By interviewing with practitioners from multiple companies, we investigated the application of using systems engineering process in ML-enabled systems. We developed a set of propositions and proposed V4ML process model for building products with ML components. We found that V4ML process model requires more efforts on documentation, system decomposition and V&V, but it addressed the interdisciplinary collaboration challenges and additional complexity introduced by ML components.

翻译：机器学习（ML）组件正被越来越多地集成到关键及高影响力的软件系统中，然而从原型ML模型到实际生产系统的软件开发过程仍然面临额外复杂度与跨学科协作挑战，这给使用传统软件生命周期模型（如瀑布模型、螺旋模型或敏捷模型）构建ML赋能系统带来了困难。通过访谈多家企业的从业者，我们探讨了在ML赋能系统中应用系统工程过程的方式。我们提出了一系列命题，并构建了用于开发含ML组件产品的V4ML过程模型。研究发现，V4ML过程模型在文档编写、系统分解及验证与确认（V&V）方面需要更多投入，但它有效解决了ML组件所带来的跨学科协作挑战和额外复杂度。