Why Good Models Fail After Deployment

Explore the critical challenges that cause machine learning models to fail after deployment in this 25-minute conference talk from Conf42 ML 2026. Discover five major failure modes that plague production ML systems, starting with data drift and how to detect and fix it when your input data changes over time. Learn about concept drift, which occurs when the underlying relationships in your data change, and label drift, where shifting base rates require model recalibration. Understand feature pipeline degradation and training-serving skew that create inconsistencies between development and production environments. Examine how feedback loops can amplify bias and degrade model performance over time. Master the essential components of building effective ML monitoring dashboards that track performance metrics, data health, and system health indicators. Compare different retraining strategies including time-based, performance-based, drift-based, and hybrid approaches to maintain model accuracy. Gain practical insights into production ML best practices that help ensure your models remain robust and reliable in real-world deployments.