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Information technologies and systems

March 29, 2024; Cambridge, UK: VI International Scientific and Practical Conference «EDUCATION AND SCIENCE OF TODAY: INTERSECTORAL ISSUES AND DEVELOPMENT OF SCIENCES»


FLASK VS. DJANGO VS. SPRING BOOT: NAVIGATING FRAMEWORK CHOICES FOR MACHINE LEARNING OBJECT DETECTION PROJECTS


DOI
https://doi.org/10.36074/logos-29.03.2024.066
Published
08.04.2024

Abstract

In the rapidly evolving field of machine learning (ML), selecting the appropriate framework for implementing object detection models is crucial for the success of a project. This article provides a comparative analysis of three popular frameworks — Flask, Django, and Spring Boot —highlighting their strengths, weaknesses, and suitability for different types of ML projects. By examining aspects such as ease of integration with ML models, performance considerations, development experience, and real-world applicability, we aim to offer insights that will guide developers in choosing the most fitting framework for their specific needs. Whether you're working on a small-scale application, a complex system requiring extensive features, or an enterprise-level solution, understanding the nuances of these frameworks will empower you to make an informed decision, thereby enhancing the efficiency and effectiveness of your object detection projects.

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