When Tests Pass but Users Fail: Scaling E2E and ML Architectures

This diary highlights that unit tests are insufficient for catching bugs born from complex real-world data interactions, necessitating a robust E2E infrastructure for proactive detection. It also provides a pragmatic framework for ML implementation, suggesting a layered fallback strategy that combines multiple tools like rembg and Rekognition with human oversight to handle edge cases.