ML Failure Modes

Hex has evolved its machine learning capabilities by detailing common failure modes in production ML systems. This includes addressing data issues like leakage, drift, and label errors, as well as issues related to shortcut learning and system complexity. The post emphasizes that most ML failures are not due to the model itself but rather surrounding architecture, processes, and organizational gaps. It provides strategies for detecting and mitigating these failures, such as monitoring input data distributions, using worst-group-accuracy for testing, and reducing system complexity through careful dependency management.