From Skipping KNN to Shipping Smart-KNN

Most ML engineers learn early to skip KNN. It’s taught as a beginner algorithm - simple , intuitive , and almost always followed by: “But you won’t use this in production.” So people move on to linear models , GBMs , neural networks… and KNN is left behind. I did the same. Until one question changed everything: What if KNN isn’t the problem - but the implementation is? That question became SmartKNN. In practice, model selection often looks like this: Need speed ? → Use linear models . Need accuracy ? → Use GBMs . KNN rarely enters production discussions because: Inference scales poorly. Memory usage is high. Latency grows with dataset size. Feature noise hurts performance. So everyone skips it. But KNN has something powerful : It memorizes reality instead of approximating it. And sometimes, that’s exactly what production needs. Smart-KNN Was Never About “Best Accuracy” Let’s be honest: There is no model that is best everywhere. Linear models are fast but limited. GBMs are accurate but