The DOI matches its Crossref record; 16 of the 16 references it cites were found in a registry or an index.
Teaching MLOps in Higher Education through Project-Based Learning
Open accessAbstract
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.
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“Some evidence also indicates improved mental health, resilience, and well-being through the social connections built working within creative collectives19though uncertainties around supporting quieter student personalities exist20.”
From Project based learning framework integrating industry collaboration to enhance student future readiness in higher education (2025) · cites this paper as [20]
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Cites
Energy and Policy Considerations for Deep Learning in NLP
Strubell, E., Ganesh, A., & McCallum, A. (2019) · Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/p19-1355 · Cited by 3,684
Model Cards for Model Reporting
Mitchell, M., Wu, S., Zaldivar, A., Barnes, P., Vasserman, L., Hutchinson, B., Spitzer, E., Raji, I. D., & Gebru, T. (2019) · Proceedings of the Conference on Fairness, Accountability, and Transparency · doi:10.1145/3287560.3287596 · Cited by 2,681 · cited 1×
“Model cards were introduced in [12] to provide a structured description of ML models including their intended use cases, ethical considerations, and limitations; they provide a template for the reporting of basic model requirements and characteristics.”
BackgroundA review of project-based learning in higher education: Student outcomes and measures
Guo, P., Saab, N., Post, L. S., & Admiraal, W. (2020) · International Journal of Educational Research · doi:10.1016/j.ijer.2020.101586 · Cited by 2,182
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Beyond accuracy: behavioral testing of NLP models with CheckList
Ribeiro, M. T., Wu, T., Guestrin, C., & Singh, S. (2020) · Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics · doi:10.18653/v1/2020.acl-main.442 · Cited by 890
Machine Teaching: A New Paradigm for Building Machine Learning SystemsPreprint
Simard, P. Y., Amershi, S., Chickering, D. M., Pelton, A. E., Ghorashi, S., Meek, C., Ramos, G., Suh, J., Verwey, J., Wang, M., et al. (2017) · arXiv (Cornell University) · doi:10.48550/arxiv.1707.06742 · Cited by 220
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DevOps for AI – Challenges in Development of AI-enabled Applications
Lwakatare, L. E., Crnković, I., & Bosch, J. (2020) · International Conference on Software, Telecommunications and Computer Networks (SoftCOM) · doi:10.23919/softcom50211.2020.9238323 · Cited by 55
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Sorted by citations
Naseer, F., Tariq, R., Alshahrani, H. M., Alruwais, N., & Al-Wesabi, F. N. (2025) · Scientific Reports · doi:10.1038/s41598-025-10385-4 · Cited by 169
“Some evidence also indicates improved mental health, resilience, and well-being through the social connections built working within creative collectives 19 though uncertainties around supporting quieter student personalities exist 20 .”
MLOps Components, Tools, Process, and Metrics: A Systematic Literature Review
Woźniak, A. P., Milczarek, M., & Woźniak, J. (2025) · IEEE Access · doi:10.1109/access.2025.3534990 · Cited by 17
An MLOps Approach for Deploying Machine Learning Models in Healthcare Systems
Mallardi, G., Calefato, F., Quaranta, L., & Lanubile, F. (2024) · IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm62325.2024.10822603 · Cited by 14
Self-citationTowards green AI-based software systems: an architecture-centric approach (GAISSA)
Martínez‐Fernández, S., Franch, X., & Durán, F. (2023) · Euromicro Conference on Software Engineering and Advanced Applications (SEAA) · doi:10.1109/seaa60479.2023.00071 · Cited by 14
Self-citationMLOps in Data Science Projects: A Review
Haertel, C., Staegemann, D. G., Daase, C., Pohl, M., Nahhas, A., & Turowski, K. (2023) · IEEE International Conference on Big Data (BigData) · doi:10.1109/bigdata59044.2023.10386139 · Cited by 15
Metadata
- Authors
- Lanubile, F., Martínez-Fernández, S., & Quaranta, L.
- Affiliations
- University of Bari Aldo Moro (IT)
- Universitat Politècnica de Catalunya,Barcelona,Spain
- Venue
- 2023 IEEE/ACM 45th International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET)
- Publisher
- IEEE
- Published
- 2023-05
- Pages
- 95-100
- OpenAlex
- W4383749422
- Cited by
- 21
- References
- 16
- Citations by year
- 2026: 5 · 2025: 8 · 2024: 3 · 2023: 5
- FWCI
- 49.61
- Type
- conference paper
- Language
- en
- Open access
- green
- arxiv.org
- Locations
- IEEE/ACM International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET) (publishedVersion)
- arXiv (Cornell University) (submittedVersion)
- UPCommons institutional repository (Universitat Politècnica de Catalunya) (submittedVersion, cc-by-nc-nd)
- CINECA IRIS Institutional Research Information System (University of Bari Aldo Moro) (submittedVersion)
- arXiv (Cornell University)
- Funders
- European Commission
- Agencia Estatal de Investigación (13039, TED2021-130923B-I00, 10.13039, AEI/10)
- NextGenerationEU
- Topics
- Social Sciences › Education › Problem and Project Based Learning
- Physical Sciences › Computer Science › Information Systems › Information Systems Education and Curriculum Development
- Social Sciences › Education › Engineering Education and Pedagogy
- Keywords
- MLOps, project-based learning, machine learning education, production deployment, model automation, course design, learning outcomes, higher education
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