I think this write-up is interesting but structurally incorrect, and everyone here is taking away the wrong conclusions. The punchline here is: > Unfortunately

I think this write-up is interesting but structurally incorrect, and everyone here is taking away the wrong conclusions. The punchline here is: > Unfortunately, once they were scaled up to production levels of use, their tradeoffs turned out to be too much to bear. These models are good—especially given how imbalanced the data for this problem is—but with too many false positives from one and too little predictive power from the other, they’re not worth betting so much money on. A practical next-step analysis would be some back-of-the-envelope economic math about the necessary detection threshold for detecting and replacing failing drives. Overall, it doesn't even make sense to even think about applying ML without understanding the cost/benefit of solving the problem. Think about Backblaze -- the drives are packed in there tightly and it's super expensive to replace one, whereas there's basically zero cost to leaving one in place. Even if you could perfectly predict which drives would fail, there's no reason you'd act on that info.