The most common trap for engineers designing ML infrastructure for the first time is thinking "can't we just deploy the model to EC2 and serve it with Flask?" It's true that deploying a model itself isn't hard. The problem comes after. When data changes, model performance silently degrades (drift). Features used during training diverge subtly from those used during inference, degrading accuracy (train-serve skew). Each time you redeploy a model, there's downtime. GPU instance costs arrive in the end-of-month bill as a shock. SageMaker is a managed platform that bundles all of this "everything after the model" into one offering