Ray Summit 2020

Distributed online learning systems are a machine learning systems that learn in a real-time, over a continuously arriving data and in a distributed manner. The challenges of distributed online learning are many, and all are non trivial. In this talk, we'll focus on the challenge of consistent and scalable maintenance of the state of learning in a fully distributed manner, with a special emphasis on how we used Ray actors to facilitate propagation of learning parameters, conflict resolution, and transactional consistency of updates. We will discuss our technical approaches and challenges in the context of our work on real-time suicide prevention predictive algorithms, fraud detection, and infectious disease surveillance.