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Front-end deep learning web apps development and deployment: a review.

Hock-Ann Goh1, Chin-Kuan Ho2, Fazly Salleh Abas1

  • 1Faculty of Engineering and Technology, Multimedia University, Jalan Ayer Keroh Lama, Bukit Beruang, 75450 Melaka Malaysia.

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Summary
This summary is machine-generated.

Front-end machine learning (ML) with TensorFlow.js enables browser-based deep learning model deployment. This approach enhances user experience and privacy by running models client-side, making ML more accessible.

Keywords:
Browser-based deep learningClient-side deep learningDeep learning web appsFront-end deep learningTensorFlow.js

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Area of Science:

  • Computer Science
  • Artificial Intelligence

Background:

  • Machine learning (ML) and deep learning (DL) models are traditionally deployed via servers or mobile apps.
  • Emerging front-end technologies like TensorFlow.js allow ML model execution directly within web browsers.

Purpose of the Study:

  • To review the development and deployment of deep learning web applications.
  • To raise awareness of advancements in client-side ML and encourage adoption.
  • To highlight the benefits of front-end ML, including improved user experience and privacy.

Main Methods:

  • Discusses the rationale for using a front-end deployment stack (JavaScript, TensorFlow.js).
  • Describes development approaches for optimizing deep learning models for front-end deployment.
  • Reviews current web applications of front-end deep learning across seven categories.

Main Results:

  • TensorFlow.js enables defining, training, and running ML models entirely in the browser.
  • Client-side deployment offers improved user interaction and data privacy.
  • Optimized models for front-end deployment require specific development considerations for size and inference speed.

Conclusions:

  • Front-end deep learning via JavaScript libraries like TensorFlow.js is a rapidly advancing field.
  • This technology offers significant potential for creating interactive, privacy-preserving ML applications.
  • The review categorizes applications, demonstrating the broad applicability of front-end deep learning.