A Comprehensive Study on Caching Mechanisms in Machine Learning Workflows

  • Authors

    • Karthik S A Manipal Institute of Technology Bengaluru, Manipal Academy of Higher Education, Manipal, India
    • Sharmila Zope Department of Computer Engineering, Jawahar Education Society's Institute of Technology Management and Research, Nashik, India
    • Venkatagurunatham Naidu Kollu Department of Artificial Intelligence and Data Science, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India 522302
    • L. Vadivukarasi Department of Mathematics, Nandha Arts and Science College, Erode, India
    • Sangeeta Borkakoty Department of Computer Science, University of Science and Technology, Meghalaya
    • Srikanth Salyan Department of Aeronautical Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka 560078
    • Bindhushree B S Department of Mechanical Engineering, Dayananda Sagar College of Engineering, Bengaluru, Karnataka
    • Krishna Nand Mishra Department of Computer Science and Engineering, Ambalika Institute of Management & Technology, Uttar Pradesh 226301
    https://doi.org/10.14419/x6r71327

    Received date: June 18, 2025

    Accepted date: August 14, 2025

    Published date: September 8, 2025

  • Machine Learning, Caching, Optimization, Performance, Challenges, Strategies, Model Training, Inference

  • Abstract

    Despite the rapidly evolving nature of machine learning (ML), caching has emerged as a critical component that significantly impacts the efficiency of model training and inference. This research explores the role of caching in ML, examining its impact, the challenges in implementation, and various approaches to optimize performance. The study aims to provide a comprehensive understanding of caching mechanisms, investigate the associated obstacles, and evaluate a range of caching algorithms. Additionally, it seeks to develop practical strategies to enhance caching efficiency within ML workflows. To achieve these objectives, a detailed literature review was conducted to analyze existing caching techniques currently employed in ML systems. Real-world case studies and experimental data were examined to assess the effectiveness of different caching solutions. Furthermore, expert interviews were conducted to gather professional insights and validate findings. The results of this study highlight the pivotal role of caching in improving the overall performance of ML systems. It proposes actionable strategies grounded in problem identification and resolution to optimize caching operations. Ultimately, the study demonstrates that overcoming caching challenges through innovative methods can lead to faster model training and more efficient implication, thus enhancing the overall effectiveness of machine learning processes.

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    S A, K. ., Zope , S. ., Kollu , V. N. ., Vadivukarasi , L. ., Borkakoty , S. ., Salyan , S. ., B S , B., & Mishra , K. N. . (2025). A Comprehensive Study on Caching Mechanisms in Machine Learning Workflows. International Journal of Basic and Applied Sciences, 14(5), 252-267. https://doi.org/10.14419/x6r71327