Online vs Offline Feature Drift: Silent Killer of ML Model Performance
Machine learning models fail in production not because they were poorly trained, but because the world they operate in changes while they remain static. Feature drift—the divergence between training data distributions and production data distributions—manifests differently depending on whether features are computed offline during training or online during inference. Understanding this distinction is critical for … Read more