Data Labelling: The Foundation of Supervised Machine Learning

source: OpenAI GPT Image 2 model You have spent three weeks tuning hyperparameters, experimenting with the latest transformer architecture, and building a slick training pipeline. Your model is finally converging. But when you run inference on real data, the predictions are off in ways that do not make sense. False positives cluster around edge cases you never considered. Certain classes are systematically misclassified. The validation accuracy looked great, yet the model fails where it matters most...