For an AI solution to evolve from a trained machine learning model into a production-ready AI system, many more things need to be considered than just the performance of the machine learning model. A production-ready AI system needs to be trustworthy, i.e. of high quality. But how to determine this in practice? For traditional software, ISO25000 and its predecessors have since long time been used to define and measure quality characteristics. Recently, quality models for AI systems, based on ISO25000, have been introduced. This paper applies one such quality model to a real-life case study: a deep learning platform for monitoring wildflowers. The paper presents three realistic scenarios sketching what it means to respectively use, extend and incrementally improve the deep learning platform for wildflower identification and counting. Next, it is shown how the quality model can be used as a structured dictionary to define quality requirements for data, model and software. Future work remains to extend the quality model with metrics, tools and best practices to aid AI engineering practitioners in implementing trustworthy AI systems.

翻译：要使AI解决方案从训练好的机器学习模型演变为可投入生产的AI系统，需要考虑的因素远不止机器学习模型本身的性能。可投入生产的AI系统必须具有可信赖性，即具备高质量属性。但在实践中如何判定其可信赖性？传统软件领域早已使用ISO25000及其前身标准来定义和度量质量特征。近年来，基于ISO25000的AI系统质量模型已被提出。本文将该质量模型应用于一个真实案例研究：用于野花监测的深度学习平台。文章描述了三个现实场景，分别勾勒了使用、扩展以及增量改进该深度学习平台进行野花识别与计数的过程。进而展示了如何将该质量模型作为结构化词典，为数据、模型和软件定义质量需求。未来仍需为该质量模型补充度量指标、工具及最佳实践，以帮助AI工程从业者实现可信赖AI系统的构建。