Machine learning (ML) teams often work on a project just to realize the performance of the model is not good enough. Indeed, the success of ML-enabled systems involves aligning data with business problems, translating them into ML tasks, experimenting with algorithms, evaluating models, capturing data from users, among others. Literature has shown that ML-enabled systems are rarely built based on precise specifications for such concerns, leading ML teams to become misaligned due to incorrect assumptions, which may affect the quality of such systems and overall project success. In order to help addressing this issue, this paper describes our work towards a perspective-based approach for specifying ML-enabled systems. The approach involves analyzing a set of 45 ML concerns grouped into five perspectives: objectives, user experience, infrastructure, model, and data. The main contribution of this paper is to provide two new artifacts that can be used to help specifying ML-enabled systems: (i) the perspective-based ML task and concern diagram and (ii) the perspective-based ML specification template.
翻译:机器学习(ML)团队往往在一个项目上工作,只是为了实现模型的性能。事实上,由ML带动的系统的成功并不足够。事实上,由ML带动的系统的成功涉及将数据与商业问题统一起来,将其转化为ML任务,对算法进行实验,评价模型,从用户那里获取数据等等。文献表明,由ML带动的系统很少基于对此类关切的精确规格而建立,导致ML团队由于错误的假设而出现错配,这可能影响到这些系统的质量和总体项目的成功。为了帮助解决这一问题,本文件描述了我们为制定基于视角的指定由ML带动的系统而开展的工作。该方法包括分析一套45 ML关注事项,分为五个角度:目标、用户经验、基础设施、模型和数据。本文的主要贡献是提供两种新的工艺品,可用于帮助指定由ML带动的系统:(一)基于视角的 ML任务和关切图表,以及(二)基于视角的 ML规格模板。