TensorFlow: Architecture, Applications, and Future Challenges

Year : 2025 | Volume : 12 | Issue : 02 | Page : 41 50
    By

    Nikita Kailas Aher,

  • Ranjana P. Dahake,

  1. Student, Department of Computer Engineering, Mumbai Educational Trust’s Bhujbal Knowledge City, Savitribai Phule Pune University, Nashik, Maharashtra, India
  2. Assistant Professor, Department of Computer Engineering, Mumbai Educational Trust’s Bhujbal Knowledge City, Savitribai Phule Pune University, Nashik, Maharashtra, India

Abstract

TensorFlow, an open-source machine learning platform created by Google, has revolutionized how artificial intelligence (AI) systems are built and implemented. Designed to support scalable and flexible model training across CPUs, GPUs, and TPUs, TensorFlow enables researchers and developers to construct advanced deep learning models with efficiency and precision. This study provides an in-depth examination of TensorFlow’s architecture, including its use of dataflow graphs and tensor-based computation. We explore its adaptability in heterogeneous environments and its extensive ecosystem of tools, such as Tensor Board, TensorFlow Lite, and TensorFlow.js, which broaden its application from cloud environments to edge devices. The framework is compatible with many types of algorithms, such as convolutional neural networks, generative adversarial networks, and reinforcement learning techniques. It is used in various fields, including computer vision, natural language processing, robotics, and healthcare. This work also discusses methodology, results, and challenges to provide a complete view of TensorFlow’s current and potential impact. While TensorFlow offers significant advantages in scalability and deployment, it also presents challenges such as a steep learning curve and resource demands. With continued advancements in distributed learning, edge computing, and model optimization, TensorFlow remains a cornerstone in the machine learning landscape. This study aims to highlight its practical implications, core capabilities, and future research directions.

Keywords: TensorFlow, deep learning, open-source frameworks, artificial intelligence, application

[This article belongs to Journal of Open Source Developments ]

How to cite this article:
Nikita Kailas Aher, Ranjana P. Dahake. TensorFlow: Architecture, Applications, and Future Challenges. Journal of Open Source Developments. 2025; 12(02):41-50.
How to cite this URL:
Nikita Kailas Aher, Ranjana P. Dahake. TensorFlow: Architecture, Applications, and Future Challenges. Journal of Open Source Developments. 2025; 12(02):41-50. Available from: https://journals.stmjournals.com/joosd/article=2025/view=222538


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Regular Issue Subscription Review Article
Volume 12
Issue 02
Received 13/05/2025
Accepted 19/05/2025
Published 13/06/2025
Publication Time 31 Days
Total Visits: 34


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