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Development of ML-enabled systems in industrial settings – An overview of practices, infrastructures, and research opportunities

Föreläsning

From: 2022-04-08 10:30 to 11:30
Place: E:1147, LTH E-huset, Ole Römers väg 3, Lund https://fileadmin.cs.lth.se/cs/Bilder/Salar/E1147.pdf
Contact: per [dot] runeson [at] cs [dot] lth [dot] se
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Dr. Lucy Ellen Lwakatare shares recent work on ML applications

Preprint: https://www.techrxiv.org/articles/preprint/Practices_and_Infrastructures_for_ML_Systems_An_Interview_Study/16939192

Abstract: The development of machine learning (ML) enabled applications in industrial settings is reported to be challenging. Prominent and experienced data-driven online organizations, like Google, are increasingly sharing knowledge of best practices and toolchains for developing ML-enabled applications. However, little is known about the state of the practice in other types of organizations. In this talk, I will present the results of our works on the state of practice in selected organizations from Sweden and Finland. The result shows a high adoption rate of some practices and tools, especially in the management of ML model experiments. However, the field needs to define and establish practices and tools for the end-to-end testing of ML systems and monitoring of ML models in production.

Short Bio: Lucy Ellen Lwakatare is a postdoc and lecturer at the University of Helsinki (UH) since 2021. Before UH, she was a postdoc at the Chalmers University of Technology, Sweden. She completed her PhD in Information Processing Science at the University of Oulu, Finland, in 2017. Her principal area of research is in software engineering process, including process tools, with a specific focus on DevOps and MLOps.