Tom Tom Founders Festival 2018

Phone apps, widgets, smart watches, and Alexa; today, machine learning integrated technology is ubiquitous in our homes, cars, devices, and phones. They exist in every domain, from product recommendation to autonomous processes like self-driving vehicles. It is possible for human decisions and choices to both augment and be influenced by machine learning algorithms that are continuously deployed, at scale, to numerous edge devices, allowing users to give and receive real-time (or near real-time) feedback. The process of building and deploying these systems, while on the surface may seem daunting, can be broken down into stages and simplified for a developer, data scientist, or analyst to build on their own. Constructing a fully deployed ML pipeline that communicates openly with the world, can best be demonstrated by something we all enjoy: Movies! A recommendation system utilizing movie data from The Movie Database and user generated data from an iPhone app will be explored both in the mathematics, data curation, and model deployment process. By the end of this talk, a listener will understand the fundamentals of recommendation engine architecture, data required to build one, and how to deploy the model to their iPhone app to collect even more data.