This talk will cover how to make millions of time series forecasts in an automated fashion. We will be covering helpful heuristics to inform preprocessing, tradeoffs between contextual evaluation metrics (and meta-metrics), useful libraries for employing different forecasting techniques in Python and R, and how to choose the best hardware for forecasting given cost and runtime constraints.