We are using machine learning to construct a failure-susceptibility ranking of feeders that supply electricity to the boroughs of New York City. The electricity system is inherently dynamic and driven by environmental conditions and other unpredictable factors, and thus the ability to cope with concept drift in real time is central to our solution. Our approach builds on the ensemble-based notion of learning from expert advice as formulated in the continuous version of the Weighted Majority algorithm [16]. Our method is able to adapt to a changing environment by periodically building and adding new machine learning models (or "experts") based on the latest data, and letting the online learning framework choose what experts to use as predictors based on recent performance. Our system is currently deployed and being tested by New York City's electricity distribution company.