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AutoML: towards automation of machine learning systems maintainability

Published: 06 December 2021 Publication History

Abstract

Machine learning systems both gained significant interest from the academic side and have seen adoption in the industry. However, one aspect that has received insufficient attention so far is the study of the lifecycle of such systems. This aspect is particularly important due to various ML systems' strong dependency on data, which is constantly evolving-and, therefore, changing-over time. The focus of my PhD research is the study of the implications of these dynamics on the ML systems' performance. Concretely, I propose a method of detecting changes caused by drift in the data early. Furthermore, I discuss possibilities for automating large parts of the ML lifecycle management, to ensure a better and more controllable maintenance process.

References

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Roberto Barros and Silas Santos. 2018. A Large-scale Comparison of Concept Drift Detectors. Information Sciences 451--452 (07 2018).
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Bilge Celik and Joaquin Vanschoren. 2020. Adaptation Strategies for Automated Machine Learning on Evolving Data.
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João Gama, I. Žliobaitė, A. Bifet, Mykola Pechenizkiy, and A. Bouchachia. 2014. A survey on concept drift adaptation. ACM Computing Surveys (CSUR) 46 (2014), 1 -- 37.
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Akanksha Kavikondala, Vivek Muppalla, Dr. Krishna Prakasha, and Vasundhara Acharya. 2019. Automated Retraining of Machine Learning Models. 8 (10 2019), 445--452.
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Jie Lu, Anjin Liu, Fan Dong, Feng Gu, Joao Gama, and Guangquan Zhang. 2019. Learning under Concept Drift: A Review. IEEE Transactions on Knowledge and Data Engineering 31, 12 (2019), 2346--2363.
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D. Sculley, Gary Holt, Daniel Golovin, Eugene Davydov, Todd Phillips, Dietmar Ebner, Vinay Chaudhary, Michael Young, Jean-Francois Crespo, and Dan Dennison. 2015. Hidden Technical Debt in Machine Learning Systems. In Proceedings of the 28th International Conference on Neural Information Processing Systems - Volume 2 (Montreal, Canada) (NIPS'15). MIT Press, Cambridge, MA, USA, 2503--2511.
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Jonathan Waring, Charlotta Lindvall, and Renato Umeton. 2020. Automated Machine Learning: Review of the State-of-the-Art and Opportunities for Healthcare. Artificial Intelligence in Medicine 104 (2020), 101822.
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Indrė Žliobaitė, Mykola Pechenizkiy, and João Gama. 2016. An Overview of Concept Drift Applications. Vol. 16. 91--114.

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  • (2022)PROBE2.0: A Systematic Framework for Routability Assessment From Technology to Design in Advanced NodesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.309301541:5(1495-1508)Online publication date: May-2022

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      cover image ACM Conferences
      Middleware '21: Proceedings of the 22nd International Middleware Conference: Doctoral Symposium
      December 2021
      38 pages
      ISBN:9781450391559
      DOI:10.1145/3491087
      This work is licensed under a Creative Commons Attribution International 4.0 License.

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      New York, NY, United States

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      Published: 06 December 2021

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      Author Tags

      1. AutoML
      2. concept drift detection
      3. data shift

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      • Short-paper

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      • ICAI lab AI for Fintech Research

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      Middleware '21
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      Middleware '21: 22nd International Middleware Conference
      December 6 - 10, 2021
      Québec city, Canada

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      Overall Acceptance Rate 203 of 948 submissions, 21%

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      • (2022)PROBE2.0: A Systematic Framework for Routability Assessment From Technology to Design in Advanced NodesIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2021.309301541:5(1495-1508)Online publication date: May-2022

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