Sustainable MLOps - Trends and Challenges

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61 Citations (Scopus)


Even simply through a GoogleTrends search it becomes clear that Machine-Learning Operations-or MLOps, for short-are climbing in interest from both a scientific and practical perspective. On the one hand, software components and middleware are proliferating to support all manners of MLOps, from AutoML (i.e., software which enables developers with limited machine-learning expertise to train high-quality models specific to their domain or data) to feature-specific ML engineering, e.g., Explainability and Interpretability. On the other hand, the more these platforms penetrate the day-to-day activities of software operations, the more the risk for AI Software becoming unsustainable from a social, technical, or organisational perspective. This paper offers a concise definition of MLOps and AI Software Sustainability and outlines key challenges in its pursuit.

Original languageEnglish
Title of host publicationProceedings - 2020 22nd International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, SYNASC 2020
Number of pages7
ISBN (Electronic)9781728176284
Publication statusPublished - Sept 2020

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FundersFunder number
European Union 's Horizon 2020 - Research and Innovation Framework Programme825480


    • DataOps
    • MLOps
    • Machine-Learning Operations
    • Software Sustainability


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