Publications

Sasu Mäkinen, Henrik Skogström, Eero Laaksonen, and Tommi Mikkonen. Who Needs MLOps: What Data Scientists Seek to Accomplish and How Can MLOps Help?. 2021. [PUMA: seminar vorteile]

Philipp Ruf, Manav Madan, Christoph Reich, and Djaffar Ould-Abdeslam. Demystifying MLOps and Presenting a Recipe for the Selection of Open-Source Tools. Applied Sciences, (11)192021. [PUMA: seminar tools] URL

Silverio Martínez-Fernández, Justus Bogner, Xavier Franch, Marc Oriol, Julien Siebert, Adam Trendowicz, Anna Maria Vollmer, and Stefan Wagner. Software Engineering for AI-Based Systems: A Survey. ACM Trans. Softw. Eng. Methodol., (31)2:1–59, Association for Computing Machinery, New York, NY, USA, Apr 1, 2022. [PUMA: definition seminar] URL

Wayne W. Ekerson, and Carsten Dr. Bange. Strategies for Driving Adoption and Usage with BI and Analytics. 2022. [PUMA: seminar statistik]

Barry Walsh. AI Best Practice and DataOps. Productionizing AI: How to Deliver AI B2B Solutions with Cloud and Python, 41--74, Apress, Berkeley, CA, 2023. [PUMA: anwendung grundlagen seminar] URL

Debmalya Biswas. Compositional AI: the future of Enterprise AI --- towardsdatascience.com. 2021. [PUMA: architektur seminar verbindung]

Debmalya Biswas. Bridging DataOps and MLOps --- towardsdatascience.com. 2022. [PUMA: seminar verbindung]

Google Cloud Architecture Center. MLOps: Continuous Delivery und Pipelines zur Automatisierung im maschinellen Lernen | Cloud Architecture Center | Google Cloud --- cloud.google.com. 2023. [PUMA: definition grundlagen seminar]

D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. Hidden Technical Debt in Machine Learning Systems. 2015. [PUMA: grundlagen seminar]

Björn Bringmann, David Thogmartin, and Peter Fach. MLOps für Unternehmen | Deloitte Deutschland --- www2.deloitte.com. 2022. [PUMA: anwendung seminar]

Peter Gluchowski. Business Analytics -- Grundlagen, Methoden und Einsatzpotenziale. HMD Praxis der Wirtschaftsinformatik, (53)3:273--286, Jun 1, 2016. [PUMA: definition grundlagen seminar] URL

IT-Daily. Mit DataOps und MLOps zu erfolgreicher KI - Onlineportal von IT Management --- it-daily.net. 2022. [PUMA: definition seminar]

Aiswarya Raj Munappy, David Issa Mattos, Jan Bosch, Helena Holmström Olsson, and Anas Dakkak. From Ad-Hoc Data Analytics to DataOps. Proceedings of the International Conference on Software and System Processes, 165–174, Association for Computing Machinery, New York, NY, USA, Sep 16, 2020. [PUMA: definition seminar] URL

Nadir Basma, Maximillian Phipps, Paul Henry, and Helen Webb. Machine Learning for Advanced Data Analytics: Challenges, use-cases and best practices to maximize business value. Machine Learning For Advanced Data Analytics: Challenges, Use-Cases And Best Practices To Maximize Business Value, 2019. [PUMA: grundlagen seminar] URL

Tim Raffin, Tobias Reichenstein, Jonas Werner, Alexander Kühl, and Jörg Franke. A reference architecture for the operationalization of machine learning models in manufacturing. Procedia CIRP, (115):130--135, Elsevier BV, 2022. [PUMA: anwendung seminar] URL

Thor Olavsrud. What is DataOps? collaborative, cross-functional analytics. CIO, December 2022. [PUMA: definition seminar] URL

Kara Sherrer. MLOps vs. devops: What are the similarities and differences?. CIO Insight, February 2023. [PUMA: seminar vergleich] URL

Chris Preimesberger. Best practices for data management using DataOps. eWEEK, February 2021. [PUMA: anwendung seminar] URL

Petteri Vainikka. 3 steps for successfully implementing industrial DataOps. Cognite I The #1 Industrial DataOps Platform, Cognite ASA, November 2022. [PUMA: anwendung seminar] URL

Dale Markowitz. Google cloud brandvoice: Why MLOps is critical to the future of your business. Forbes, Forbes Magazine, July 2021. [PUMA: anwendung seminar] URL