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Disk drives are one of the most commonly replaced hardware components and continue to pose challenges for accurate failure prediction. In this work, we present analysis and findings from one of the largest disk failure prediction studies covering a total of 380,000 hard drives over a period of two months across 64 sites of a large leading data center operator. Our proposed machine learning based models predict disk failures with 0.95 F-measure and 0.95 Matthews correlation coefficient (MCC) for 10-days prediction horizon on average.