Data & requirements: The twin pillars of ML disappointment¶

Data is the “fuel” of machine learning, except when it’s more like damp kindling that refuses to catch fire. And requirements are those pesky constraints that exist to remind you that, no, you can’t just “YOLO” your way into production. Together, they form the foundation of your MLOps journey, ensuring that your models are either flawed (if you’re lucky) or scandalous (if you’re not).

Data and requirements are the twin gods of MLOps, demanding sacrifices of time, sanity, and caffeine. Will your data be perfect? No. Will your requirements be realistic? Don’t be naïve. But if you squint, cross your fingers, and ignore most of the problems, you might just ship something that works—or at least fails in a way that’s legally defensible.

Now go forth. May your datasets be balanced, your bias checks thorough, and your regulatory fines minimal. (They won’t be.)