Comparative Study on the Efficiency of Deep Learning Model Training in Cloud Environments: Google Colab vs AWS

Oki Arifin; Fauzan  Azim; Yuli  Hartati; Dewi Kania Widyawati; Ahmad Luqman Ahmad Kamal  Ariffin

doi:10.51454/decode.v5i2.1197

Authors

Oki Arifin Department of Software Engineering Technology Politeknik Negeri Lampung
Fauzan Azim Department of Informatics Engineering Universitas Muhammadiyah Riau
Yuli Hartati Department of Information Systems Institut Teknologi dan Ilmu Sosial Khatulistiwa
Dewi Kania Widyawati Department of Informatics Management Politeknik Negeri Lampung
Ahmad Luqman Ahmad Kamal Ariffin Faculty of Information Sciences and Engineering Management and Science University

DOI:

https://doi.org/10.51454/decode.v5i2.1197

Keywords:

Amazon Web Services (AWS), Cloud Computing, Deep Learning, Google Colab, Training Efficiency Model

Abstract

Deep learning has become a major foundation in the development of modern artificial intelligence technologies, especially in the applications of image recognition, natural language processing, and recommendation systems. However, the training process of deep learning models requires large and efficient computing resources. This study aims to evaluate the efficiency of training deep learning models on two popular cloud platforms, namely Google Colab and Amazon Web Services (AWS). The method used is a comparative experiment with a simple Convolutional Neural Network (CNN) model trained using the CIFAR-10 dataset, and Identical training hyperparameters were applied on both platforms. The results show that Google Colab demonstrates greater cost efficiency as it provides GPUs for free, while AWS provides faster training performance and slightly higher validation accuracy. This study concludes that platform selection should be tailored to the user's needs, both in terms of budget, project scale, and system stability. These findings offer preliminary guidance for selecting cloud platforms in small- to medium-scale deep learning projects.

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