Deep Learning Integration for Image Processing on Cloud Platforms: Enhancing Accuracy and Processing Speed through TensorFlow and PyTorch - Kauser, Syeda Zeba; Sable, Dr Archana Harsing (International Journal of Aquatic Research and Environmental Studies, 2026)
This research presents a comparative evaluation of TensorFlow and PyTorch for deep learning integration on the cloud with respect to training speed, computational synergies, scalability, and accuracy. The experiments performed used identical datasets and cloud configurations to measure the time to train the algorithms, the time to inference, the amount of GPU used during training, and the price-performance. The findings suggested that TensorFlow achieved fast convergence, expended the least number of resources, and benefitted from good hardware optimization; therefore it was the most effective framework for training and deploying on a large production scale. Conversely, PyTorch portrayed almost equivalent accuracy but offered significantly more ease of use and dynamic computation graph capabilities, and was extraordinarily beneficial for rapid and iterative experimentation (such as prototyping new research applications). This study directly addressed the research question of which framework was best suited to meet the demands of this AI task in the cloud by showing that TensorFlow is suited for enterprise-grade work, and PyTorch is particularly suited for research-driven applications. The scope for future work includes investigating hybrid TensorFlow–PyTorch workflows, connecting with explainable AI (XAI) tools and developing optimised work flows in edge-cloud collaborative systems that improve performance and transparency.
Keywords: Deep Learning, TensorFlow, PyTorch, Cloud Computing, Training Speed, Inference Latency, Scalability, Explainable AI (XAI), Edge-Cloud Integration, Prototyping, Production Deployment.