Target leaks occur whenever unexpected additional information is present in training data, allowing the model to make unrealistically good predictions... until you deploy it. Drawing from client engagement experiences at SparkBeyond, I will present real-world examples of hidden, tricky, and non-obvious target leaks, as well as strategies to identify them before your model hits production.