Lift: A General Purpose Architecture for Scalable, Realtime Machine Learning

In the last few years, we’ve witnessed explosive growth in the role machine learning (ML) plays in technology. Making good predictions from data has always been important in our industry, but modern machine learning techniques allow us to be much more systematic. However, this wealth of new ML algorithms and services present new challenges for software developers.   (more)

Using Artificial Neural Networks to Analyze Trends of a Global Aircraft Fleet

I’m an intern in the Commercial Mobility group at ViaSat.  Our group is responsible for all of the company’s commercial aviation clients, providing internet services to aircraft. While providing the world’s best in-flight internet service to airplanes traveling over 500 miles per hour 30,000 feet above the ground is no small feat, it is also a challenge to analyze and predict user demand of our network. There are typically several hundred planes connected to ViaSat’s network at any given time amounting to 15,000-40,000 flights a week depending on the season. With this much range and traffic, and flights leaving all times of day, all over the world, modeling anything about them becomes very difficult.