Why Model Deployments Need Quality Gates, Not Just Health Checks

Unlike code deployments that fail loudly, machine learning model rollouts can silently degrade in quality — returning normal latency and status codes while performing measurably worse. A 2% drop in accuracy, for instance, produces no errors or alerts and may only surface as support tickets weeks later. Engineers deploying models must choose between blue-green, canary, and shadow strategies, each with distinct tradeoffs around cost, risk, and the time needed to gather statistically meaningful signal. The recommended approach is to compose all three: shadow traffic first to catch crashes, then canary to detect subtler regressions, before a full promotion. Traffic splits must also be sticky per user — not per request — to avoid inconsistent experiences and contaminated metrics during evaluation.