6 Little-Known Challenges After Deploying Machine Learning

Eugene Yan · 6 years ago

Deploying machine learning models creates six categories of post-deployment challenges: data schema changes that degrade model performance, unwanted interactions between models and features, messy infrastructure and configuration management, real-world data drift and adversarial attacks, organizational conflicts between data science and engineering teams, and customer support obligations. The author highlights specific technical issues like data leakage in hospital billing models, redundant features accumulating over time, and feedback loops where model predictions influence future training data. These challenges require systematic monitoring and organizational alignment to prevent production failures and maintain model performance over time.