Cloud-Based Deep Learning: Technologies, Challenges and Impact on Modern Data Science

This paper presents a thorough examination of cloud-based deep learning, with a focus on pivotal aspects including the comparison of cloud-based and traditional GPUs, model training optimization, and infrastructure management. Emerging trends such as transfer learning, federated learning, and automated machine learning are also explored, in addition to integration with other cloud tools. Furthermore, the paper addresses ethical considerations, including bias, fairness, and transparency, as well as cost-effectiveness and real-world applications. The paper concludes with an analysis of prospects and technological innovations that will shape the evolution of cloud-based deep learning.