Why your ML model's 95% accuracy is just the start

A model scoring 95% on test sets often fails in production due to training-serving skew, data drift, and missing retraining pipelines. The engineering around the model—monitoring, CI/CD, and business metrics—accounts for 90% of the work. Teams that treat the model as one part of a production system, not the finish line, create real value.