Predictive Maintenance is about anticipating failures and taking preemptive actions. In the realm of predictive maintenance, the event of interest is an equipment failure. Modelling for Predictive Maintenance falls under the classic problem of modelling with imbalanced data when only a fraction of the data constitutes failure. This kind of data poses several issues. In this talk I will highlight some of the pitfalls and challenges of building a model with such data and describe ways to circumvent the problems using real use cases and examples.