Cloud-Based Deep Learning: End-To-End Full-Stack Handwritten Digit Recognition

Herein, we present Stratus, an end-to-end full-stack deep learning application deployed on the cloud. The rise of productionized deep learning necessitates infrastructure in the cloud that can provide such service (IaaS). In this paper, we explore the use of modern cloud infrastructure and micro-ser...

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Bibliographic Details
Published inarXiv.org
Main Authors Zeng, Ruida, Jha, Aadarsh, Kumar, Ashwin, Luo, Terry
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 01.02.2023
Subjects
Artificial neural networks
Deep learning
Handwriting recognition
Infrastructure
Machine learning
Tooling
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Summary:Herein, we present Stratus, an end-to-end full-stack deep learning application deployed on the cloud. The rise of productionized deep learning necessitates infrastructure in the cloud that can provide such service (IaaS). In this paper, we explore the use of modern cloud infrastructure and micro-services to deliver accurate and high-speed predictions to an end-user, using a Deep Neural Network (DNN) to predict handwritten digit input, interfaced via a full-stack application. We survey tooling from Spark ML, Apache Kafka, Chameleon Cloud, Ansible, Vagrant, Python Flask, Docker, and Kubernetes in order to realize this machine learning pipeline. Through our cloud-based approach, we are able to demonstrate benchmark performance on the MNIST dataset with a deep learning model.
ISSN:2331-8422