Beyond Accuracy: The Systems Thinking Gap in ML Engineering

Most ML engineers optimize models in isolation, but production systems fail due to data pipeline issues, insufficient monitoring, and poor system design—factors rarely taught in academic ML programs. This skill gap is becoming critical as AI moves into mission-critical applications where end-to-end system reliability matters more than marginal accuracy improvements.