An Overview of Web-Based Machine Learning Frameworks

Author(s)	Vishakha Agrawal
Country	United States
Abstract	The rapid convergence of machine learning (ML) and web technologies has given rise to a new generation of web-based ML frameworks, revolutionizing the deployment and accessibility of AI capabilities. This paper provides an in- depth examination of the current landscape of web-based ML frameworks, delving into their architectural designs, functional capabilities, and diverse applications in modern web develop- ment. We investigate how these frameworks facilitate the seamless deployment and inference of ML models directly within web browsers, thereby democratizing access to AI-driven insights while mitigating privacy concerns and minimizing server-side dependencies. By exploring the benefits, challenges, and future directions of web-based ML frameworks, this overview aims to provide a comprehensive understanding of the transformative potential of AI in the web ecosystem.
Keywords	WebAssembly, WebGL, TensorFlow.js, ONNX.js, ML5.js, Real-time object detection system, Natural Language Processing
Field	Engineering
Published In	Volume 13, Issue 2, July-December 2022
Published On	2022-12-06
Cite This	An Overview of Web-Based Machine Learning Frameworks - Vishakha Agrawal - IJAIDR Volume 13, Issue 2, July-December 2022. DOI 10.5281/zenodo.14684704
DOI	https://doi.org/10.5281/zenodo.14684704
Short DOI	https://doi.org/g82cb3

About IJAIDR Fees & Payment Current Issue Publication Archive	Submit Research Paper Track Submission Status Publication Guidelines Publication Ethics Peer Review & Plagiarism	Join as a Reviewer Editors & Reviewers Reviewer Referral Program Get Reviewer Membership Certi.	Website/Journal Policies Usage Policy Content Policies Privacy Policy

Contact Us	Message on WhatsApp	+91-9687-182-185	editor@ijaidr.com

Journal of Advances in Developmental Research