Experience Report from a Graduate ML Production Systems Course

There is a dearth of coursework on designing and implementing services that incorporate machine learning models in university curricula despite the growing industry demand. We describe the design of a course titled "ML Production Systems" which covers the implementation, deployment, monitoring, and updating of machine learning models as part of user-facing web services. The course is designed around a semester-long project to implement and deploy a home sale price prediction service. The course is a required course in a Master’s in Machine Learning program and graduate Machine Learning Engineering certificate program. The course was taught in an online synchronous format to twenty-nine students in Fall 2023; fifteen of the students had more than one year of professional experience as software engineers, while the remaining fourteen students were accelerated Master’s students. Analysis of open-ended written student feedback indicated that students in both groups had a positive experience and found the course to be valuable. Assessment of learning outcome achievements with a final exam and project completion rates indicated that nearly all of the students successfully achieved the learning outcomes. No statistically-significant differences in achievement between the working professionals and accelerated Master’s students were detected. We believe that this course and its successful delivery will serve as a blueprint for other faculty who may want to implement similar courses.