# Teaching MLOps in Higher Education through Project-Based Learning

arXiv: https://arxiv.org/abs/2302.01048 · HTML: https://mlsanity.com/paper/2302.01048/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 31 references that are in the map.
- First posted on arXiv 2 Feb 2023.

## Builds on (most cited first, 9 of 9)

- [Energy and Policy Considerations for Deep Learning in NLP](https://mlsanity.com/paper/1906.02243/) (2019)
- [Model Cards for Model Reporting](https://mlsanity.com/paper/1810.03993/) (2018)
- [Beyond Accuracy: Behavioral Testing of NLP models with CheckList](https://mlsanity.com/paper/2005.04118/) (2020)
- [Green AI](https://mlsanity.com/paper/1907.10597/) (2019)
- [Machine Teaching: A New Paradigm for Building Machine Learning Systems](https://mlsanity.com/paper/1707.06742/) (2017)
- [Teaching Software Engineering for AI-Enabled Systems](https://mlsanity.com/paper/2001.06691/) (2020)
- [Eliciting Best Practices for Collaboration with Computational Notebooks](https://mlsanity.com/paper/2202.07233/) (2022)
- [Lessons Learned from Educating AI Engineers](https://mlsanity.com/paper/2103.10703/) (2021)
- [Pynblint: a Static Analyzer for Python Jupyter Notebooks](https://mlsanity.com/paper/2205.11934/) (2022)

## Built on by (most cited first, 3 of 3)

- [Towards green AI-based software systems: an architecture-centric approach (GAISSA)](https://mlsanity.com/paper/2307.09964/) (2023)
- [Addressing Quality Challenges in Deep Learning: The Role of MLOps and Domain Knowledge](https://mlsanity.com/paper/2501.08402/) (2025)
- [Scaffolding Collaborative Learning in STEM: A Two-Year Evaluation of a Tool-Integrated Project-Based Methodology](https://mlsanity.com/paper/2509.02355/) (2025)

## Abstract (arXiv)

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.