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MLadder: An Online Training System for Machine Learning and Data Science Education

Published: 17 October 2022 Publication History
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  • Abstract

    Education on machine learning and data science has drawn a lot of attention in both higher education and vocational training. Although various tools and services such as Jupyter Notebook and Google Cloud's AI have been developed for building and training models, they are not suitable for direct use in educational settings. For example, teachers expect a platform where they can easily distribute and grade programming assignments, and students want to quickly start coding and training models without the burden of setting up an environment. To this end, we develop MLadder, an online training system for machine learning and data science education. Specifically, we seamlessly integrate two open-source software, CodaLab and Jupyter Notebook, which are used for hosting assignments and building models, respectively. Moreover, we devise several methods to make the system lightweight and scalable, so that it can be deployed on-premises even with limited resources. We have used MLadder in the machine learning and data science courses in our school and facilitated both teaching and learning.

    Supplementary Material

    MP4 File (CIKM22-demo138.mp4)
    We explained in detail the three main aspects of MLadder: background, system design, and resource management. And we provide the video of the system demonstration.

    References

    [1]
    David Bernstein. 2014. Containers and cloud: From lxc to docker to kubernetes. IEEE Cloud Computing, Vol. 1, 3 (2014), 81--84.
    [2]
    Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. 2009. Imagenet: A large-scale hierarchical image database. In 2009 IEEE conference on computer vision and pattern recognition. Ieee, 248--255.
    [3]
    Miguel Grinberg. 2018. Flask web development: developing web applications with python. " O'Reilly Media, Inc.".
    [4]
    Torge Hinrichs, Henri Bureau, Jens von Pilgrim, and Axel Schmolitzky. 2021. A Scaleable Online Programming Platform for Software Engineering Education. In Software Engineering (Satellite Events).
    [5]
    Thomas Kluyver, Benjamin Ragan-Kelley, Fernando Pérez, Brian Granger, Matthias Bussonnier, Jonathan Frederic, Kyle Kelley, Jessica Hamrick, Jason Grout, Sylvain Corlay, et al. 2016. Jupyter Notebooks--a publishing format for reproducible computational workflows. In Positioning and Power in Academic Publishing: Players, Agents and Agendas. IOS Press, 87--90.
    [6]
    Stephan Krusche and Andreas Seitz. 2018. Artemis: An automatic assessment management system for interactive learning. In Proceedings of the 49th ACM technical symposium on computer science education. 284--289.
    [7]
    Dirk Merkel et al. 2014. Docker: lightweight linux containers for consistent development and deployment. Linux journal, Vol. 2014, 239 (2014), 2.
    [8]
    Adrien Pavao, Isabelle Guyon, Anne-Catherine Letournel, Xavier Baró, Hugo Escalante, Sergio Escalera, Tyler Thomas, and Zhen Xu. 2022. CodaLab Competitions: An open source platform to organize scientific challenges. Technical report (2022). https://hal.inria.fr/hal-03629462v1
    [9]
    Karen Simonyan and Andrew Zisserman. 2015. Very Deep Convolutional Networks for Large-Scale Image Recognition. In 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7--9, 2015, Conference Track Proceedings, Yoshua Bengio and Yann LeCun (Eds.). http://arxiv.org/abs/1409.1556
    [10]
    Szymon Wasik, Maciej Antczak, Jan Badura, Artur Laskowski, and Tomasz Sternal. 2018. A survey on online judge systems and their applications. ACM Computing Surveys (CSUR), Vol. 51, 1 (2018), 1--34.

    Cited By

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    • (2023)Cloud-Operated Open Literate Educational Resources: The Case of the MyBinderIEEE Transactions on Learning Technologies10.1109/TLT.2023.334369017(893-902)Online publication date: 19-Dec-2023
    • (2023)Assessing Human Activity Recognition Performances of Different Machine Learning Algorithms Using Sensor Data2023 IEEE Silchar Subsection Conference (SILCON)10.1109/SILCON59133.2023.10404163(1-6)Online publication date: 3-Nov-2023

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    1. MLadder: An Online Training System for Machine Learning and Data Science Education

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      Published In

      cover image ACM Conferences
      CIKM '22: Proceedings of the 31st ACM International Conference on Information & Knowledge Management
      October 2022
      5274 pages
      ISBN:9781450392365
      DOI:10.1145/3511808
      • General Chairs:
      • Mohammad Al Hasan,
      • Li Xiong
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than the author(s) must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected].

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      New York, NY, United States

      Publication History

      Published: 17 October 2022

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      Author Tags

      1. educational support
      2. kubernetes
      3. machine learning education
      4. online systems

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      Funding Sources

      • National Natural Science Foundation of China

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      CIKM '22
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      CIKM '22 Paper Acceptance Rate 621 of 2,257 submissions, 28%;
      Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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      View all
      • (2023)Cloud-Operated Open Literate Educational Resources: The Case of the MyBinderIEEE Transactions on Learning Technologies10.1109/TLT.2023.334369017(893-902)Online publication date: 19-Dec-2023
      • (2023)Assessing Human Activity Recognition Performances of Different Machine Learning Algorithms Using Sensor Data2023 IEEE Silchar Subsection Conference (SILCON)10.1109/SILCON59133.2023.10404163(1-6)Online publication date: 3-Nov-2023

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