Mobile Cloud Computing for the Data Scientist*
An explanation of the key differences and problems that mobile cloud computing faces, as well as solutions to address some of these immediate challenges. A walk-through in the architecture of a large-scale mobile cloud, as well as a how-to explanation. We will then run a simple machine learning program, and explain where the data is being fetched from the cloud and how this data is being handled. We will then discuss what innovate smart apps do and how these apps take it to the next level.
As the market for smart phones and tablets grow, so do the challenges in integrating these devices. A notable issue with the mobile cloud is the resource poverty of mobile devices. They have less screen real estate, less memory, less compute power, less battery capacity, and sometimes limited bandwidth. For these reasons, the mobile cloud is often viewed as an SaaS cloud, meaning that computation and data handling is done remotely. Even with these challenges, the mobile computing market has grown with the help of numerous enabling technologies such as 4G, HTML5, and CSS3, and embedded hypervisor. In this discussion, I will go beyond the issues and teach you how to build and maintain a mobile cloud suitable for a machine-learning intensive app using OpenStack technology. I will give step-by-step instructions and tips learned while building a smart app, which I will showcase at the end.
mobile, cloud, computing, saas, infrastructure, machine, learning, data, science
While I have not spoken in a conference before, I have won multiple speech and debate tournaments throughout high school, speaking for as many as 150 people.
Hello, it is nice to meet you! I am an energetic and studious person. While not an artist, I do believe in the art of clean code. I am a recent Cornell graduate, where I majored in Computer Science. Currently, I am working as a Cloud Engineer and in my spare-time enjoy developing side-projects on new and innovative technologies.