Integrating Machine Learning Technologies to Enhance Web Development Efficiency

Anastasiia Perih

Citation: Anastasiia Perih, "Integrating Machine Learning Technologies to Enhance Web Development Efficiency", Universal Library of Engineering Technology, Volume 02, Issue 01.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

This article explores the application of machine learning technologies in the development of modern web applications, using JavaScript for the frontend, Python for the backend, and Amazon Web Services (AWS) for cloud infrastructure. Given the growing user expectations around personalization and responsive interfaces, the focus is on solutions that extend beyond static architectures. The review encompasses both open-source projects and production-level implementations, enabling the identification of architectural patterns for integrating machine learning into the technology stack. The article examines methods for processing user interactions with TensorFlow.js and ml5.js, the use of trainable models in server-side logic built with Flask, and the deployment of AWS tools—such as Rekognition for computer vision, Lex for conversational interfaces, and SageMaker for model deployment. Additionally, it highlights the potential of AI-driven tools that optimize routine stages of development—for instance, automated code and test script generation using GitHub Copilot and TestIM. The study aimed to evaluate the practical value of machine learning as a means of enhancing flexibility, reducing development time, and increasing the reliability of web applications. The methodology included analysis of documentation, open-source codebases, and empirical observation of team workflows. The materials presented are intended for professionals involved in the design, implementation, and maintenance of web systems.


Keywords: Machine Learning, Web Development, Frontend, Backend, Tensorflow.Js, AWS, Test Automation, AI Tools, Personalization, CI/CD.

Download doi https://doi.org/10.70315/uloap.ulete.2025.0201010