Teaching MLOps in Higher Education through Project-Based Learning
This addresses the problem of preparing students in higher education for real-world ML deployment, though it is incremental as it adapts existing educational methods to a specific domain.
The paper tackles the gap between academic ML education and production needs by introducing a project-based learning approach for teaching MLOps, focusing on automating ML component construction and covering the full life cycle from model building to deployment, with preliminary results from a first course edition and ongoing evaluation in two universities.
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.