Manual annotation is a massive bottleneck for multimodal inference systems in high-velocity production environments. If you want to survive catastrophic distribution shifts, you have to automate your labeling pipeline. I want to walk through a pseudo-labeling architecture we built that filters out extreme pipeline noise to hit a 0.93 F1 score using XGBoost.Semi-supervised strategies like pseudo-labeling look great on paper but often fail in practice. They suffer from confirmation bias. The model just repeatedly overfits to its own bad predictions because it is overly confident in them. This triggers catastrophic pipeline noise and runaway concept drift (where the underlying statistical properties of your target variable change over time and destroy your predictive accuracy).