一句话：设计并首次讲授一门面向 ML 背景学生的软件工程课程，强调真实数据、可演进基础设施与伦理需求工程。
论文针对构建含人工智能/机器学习（ML）组件的系统所面临的新工程挑战，以及 ML 背景学生缺乏软件工程训练的问题，设计了一门新的软件工程课程。课程超越在人工条件下讲授建模技术的传统 ML 课程，在讲座和作业中强调真实场景：大规模且不断变化的数据集、健壮且可演进的基础设施，以及兼顾伦理与公平的需求工程。作者介绍了课程设计与配套基础设施，并分享首次授课的经验和全部教学材料。
Software engineers have significant expertise to offer when building intelligent systems, drawing on decades of experience and methods for building systems that are scalable, responsive and robust, even when built on unreliable components. Systems with artificial-intelligence or machine-learning (ML) components raise new challenges and require careful engineering. We designed a new course to teach software-engineering skills to students with a background in ML. We specifically go beyond traditional ML courses that teach modeling techniques under artificial conditions and focus, in lecture and assignments, on realism with large and changing datasets, robust and evolvable infrastructure, and purposeful requirements engineering that considers ethics and fairness as well. We describe the course and our infrastructure and share experience and all material from teaching the course for the first time.