Building and maintaining production-grade ML-enabled components is a complex endeavor that goes beyond the current approach of academic education, focused on the optimization of ML model performance in the lab. In this paper, we present a project-based learning approach to teaching MLOps, focused on the demonstration and experience with emerging practices and tools to automatize the construction of ML-enabled components. We examine the design of a course based on this approach, including laboratory sessions that cover the end-to-end ML component life cycle, from model building to production deployment. Moreover, we report on preliminary results from the first edition of the course. During the present year, an updated version of the same course is being delivered in two independent universities; the related learning outcomes will be evaluated to analyze the effectiveness of project-based learning for this specific subject.

翻译：构建和维护符合生产标准的机器学习组件是一项复杂的工作，其要求已超越当前侧重于实验室环境下优化机器学习模型性能的高等教育模式。本文提出了一种基于项目式学习的MLOps教学方法，重点在于通过实践与演示，让学生体验并掌握用于实现机器学习组件构建自动化的新兴实践与工具。我们围绕该方法设计了一门课程，包括覆盖机器学习组件全生命周期（从模型构建到生产部署）的实验课。此外，本文还报告了该课程首届授课的初步成果。本年度，该课程的更新版本正在两所独立大学同步开展；其相关学习成效将用于评估基于项目式学习对该特定课程的有效性。