The most common shock teams hit 6 months after pushing an ML model to production is: "the model silently started failing." The code unchanged, infrastructure healthy, error logs clean. Yet recommendation click-through drops, fraud detection misses new patterns. The cause isn't code—it's that the world changed. Input data distribution shifted from training time (data drift), or the outcome pattern itself changed (concept drift). Traditional software operations (DevOps) have no tools for this "silent degradation." MLOps addresses ML systems' unique problem—data dependency, drift, train-serve skew, automated retraining—as operational discipline