3 Tell us if this sounds a little too familiar: You’ve spent months working on building a machine learning (ML) model, you’ve found the right data to train it, you’ve tested it, and now it’s in production. Everything’s going great, and everyone’s happy — until, that is, you start to see small cracks. Maybe you get a false positive, something seemingly small at first. Over time, you start noticing that these small errors are happening more o!en, and suddenly your predictions are significantly less accurate than they were. What happened? Any number of things. ML models are complex and multi-faceted, and as such, have multiple potential failure points. Much like any machine with moving parts, an error in one point could create unforeseen problems elsewhere. Maybe the data is a litt

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