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

Published: 17 October 2022 Publication History

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.

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Cited By

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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  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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    CIKM '25

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    Cited By

    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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