  Existing machine learning approaches for data-driven predictive maintenance
are usually black boxes that claim high predictive power yet cannot be
understood by humans. This limits the ability of humans to use these models to
derive insights and understanding of the underlying failure mechanisms, and
also limits the degree of confidence that can be placed in such a system to
perform well on future data. We consider the task of predicting hard drive
failure in a data center using recent algorithms for interpretable machine
learning. We demonstrate that these methods provide meaningful insights about
short- and long-term drive health, while also maintaining high predictive
performance. We also show that these analyses still deliver useful insights
even when limited historical data is available, enabling their use in
situations where data collection has only recently begun.
