Methods: This work introduces a method supporting the collaborative definition of machine learning tasks by leveraging model-based engineering in the formalization of the systems modeling language SysML. The method supports the identification and integration of various data sources, the required definition of semantic connections between data attributes, and the definition of data processing steps within the machine learning support. Results: By consolidating the knowledge of domain and machine learning experts, a powerful tool to describe machine learning tasks by formalizing knowledge using the systems modeling language SysML is introduced. The method is evaluated based on two use cases, i.e., a smart weather system that allows to predict weather forecasts based on sensor data, and a waste prevention case for 3D printer filament that cancels the printing if the intended result cannot be achieved (image processing). Further, a user study is conducted to gather insights of potential users regarding perceived workload and usability of the elaborated method. Conclusion: Integrating machine learning-specific properties in systems engineering techniques allows non-data scientists to understand formalized knowledge and define specific aspects of a machine learning problem, document knowledge on the data, and to further support data scientists to use the formalized knowledge as input for an implementation using (semi-) automatic code generation. In this respect, this work contributes by consolidating knowledge from various domains and therefore, fosters the integration of machine learning in industry by involving several stakeholders.

翻译：方法：本文提出一种方法，通过利用基于模型的工程在系统建模语言SysML的形式化过程中，支持机器学习任务的协作定义。该方法支持多种数据源的识别与整合、数据属性间语义关联的必要定义，以及在机器学习支持框架内定义数据处理步骤。结果：通过整合领域专家与机器学习专家的知识，提出了一种利用系统建模语言SysML形式化知识以描述机器学习任务的强大工具。该方法基于两个用例进行评估：一是基于传感器数据预测天气预报的智能气象系统，二是针对3D打印耗材的废弃物预防案例（图像处理），若无法达到预期效果则取消打印。此外，还开展了一项用户研究，以收集潜在用户对所设计方法的感知工作负荷与可用性的反馈。结论：将机器学习特定属性融入系统工程方法中，能使非数据科学家理解形式化知识、定义机器学习问题的具体方面、记录数据相关知识，并进一步支持数据科学家将形式化知识作为使用（半）自动代码生成进行实现的输入。在此方面，本文通过整合不同领域的知识，促进了多方利益相关者参与下的机器学习工业化集成。