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Analysis on Trends of No-Code Machine Learning Tools

  • Yo-Seob, Lee (School of ICT Convergence, Pyeongtaek University) ;
  • Phil-Joo, Moon (Dept. of Information & Communications, Pyeongtaek University)
  • Received : 2022.10.29
  • Accepted : 2022.11.30
  • Published : 2022.12.31

Abstract

The amount of digital text data is growing exponentially, and many machine learning solutions are being used to monitor and manage this data. Artificial intelligence and machine learning are used in many areas of our daily lives, but the underlying processes and concepts are not easy for most people to understand. At a time when many experts are needed to run a machine learning solution, no-code machine learning tools are a good solution. No-code machine learning tools is a platform that enables machine learning functions to be performed without engineers or developers. The latest No-Code machine learning tools run in your browser, so you don't need to install any additional software, and the simple GUI interface makes them easy to use. Using these platforms can save you a lot of money and time because there is less skill and less code to write. No-Code machine learning tools make it easy to understand artificial intelligence and machine learning. In this paper, we examine No-Code machine learning tools and compare their features.

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References

  1. J. Shalamanov, "2022's Most Relevant Machine Learning Trends," https://www.udacity.com/blog/2022/06/2022s-most-relevant-machine-learning-trends.html. 
  2. Y. Lee, "Analysis on trends of machine learning-as-a-service," Vol. 6, No. 4, International Journal of Advanced Culture Technology, 2018, https://doi.org /10.17703//IJACT2018.6.4.303 
  3. Y. Lee and P. Moon, "Analysis of Machine Learning Education Tool for Kids," Vol.8, No.4, International Journal of Advanced Culture Technology, 2020, https://doi.org/10.17703/IJACT.2020.8.4.235 
  4. E. Ozan, "A Novel Browser-based No-code Machine Learning Application Development Tool, " 2021 IEEE World AI IoT Congress (AIIoT), May 2021. https://doi.org/10.1109/AIIoT52608.2021.9454239 
  5. E. Ozan, ML_Tool, 2021, https://github.com/ozanix/ml_tool. 
  6. C.V.Krishnakumar Iyer, F. Hou, H. Wang, Y. Wang, K. Oh, S. Ganguli and V. Pandey, "Trinity: A NoCode AI platform for complex spatial datasets," https://arxiv.org/abs/2106.11756, https://doi.org/10.48550/arXiv.2106.11756 
  7. H. Vora, H. Mirani and V. Bhatt, "Traditional Machine Learning and No-Code Machine Learning with its Features and Application," International Journal of Trend in Scientific Research and Development, Volume 5 Issue 2, 2021. 
  8. H. Kleinings, "No-Code Machine Learning: The Ultimate Guide," https://levity.ai/blog/no-code-machine-learning-guide. 
  9. Teachable Machine, https://teachablemachine.withgoogle.com/. 
  10. StackML, https://stackml.com/