  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.
