Cloud computing for deep learning analytics:A survey of current trends and challenges

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Anam Saiyeda
Mansoor Ahmad Mir


Deep learning, a sub-field of machine learning is inspired by the principle of information processing in the human brain. Its applications are autonomous driving, robotics control, machine translation etc. It needs multiple training examples of the task, specialized GPU hardware, capital investment and its libraries evolve quickly, so frequent updates are needed. Cloud computing is a type of computing in which computing resources are provided on demand. Cloud is an apt choice for a platform for deep learning analytics as it provides servers, storage and networking resources. It provides scalability, processing, storage and analytics resources. Deep learning algorithms like CNN are computationally intensive for a commercial computer for large larger datasets. Cloud computing allows prevailing the processing, memory constraints of average computers, allowing computations on larger datasets. This paper discusses how cloud computing allows us to overcome the constraints of deep learning analytics on average systems and the various platforms offered by various providers. Google, NVIDIA, IBM provide us platforms for deep learning. IBM’s Rescale, Nervana Cloud , Google deep learning cloud provide full-stack hosted platform for deep learning.
Keywords: Deep learning, Artificial intelligence, scalability, virtualization


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