QuestionQ198
Deployment StrategiesA Machine Learning Engineer has deployed a real-time fraud-detection model that approves or blocks millions of transactions each day. They must deploy a new version of the model with improved detection accuracy to this high-traffic, business-critical application. Because any downtime of the model could cause lost revenue or customer dissatisfaction, the engineer must provide zero downtime and minimal end-user disruption. Leadership also requires an immediate rollback to the prior version if issues are detected with the new model in production.
Which deployment strategy satisfies these requirements?
- A Use a canary deployment by initially routing a small percentage of user traffic to the new model version, monitoring results, and gradually increasing exposure until all traffic uses the new version if no problems are detected.
- B Replace the current production model with the new version during a scheduled maintenance window, notify affected users of potential brief disruptions, and prepare to reroute requests to a backup if failures occur after deployment.
- C Use a blue-green deployment for the new model, maintaining two separate production environments (one “blue,” one “green”) and switching user traffic to the new version only after confirming it is healthy, enabling instant rollback if needed.
- D Deploy the new model in parallel with the old version, monitor both for a set period, and then notify all stakeholders to manually switch over to the new version at a coordinated time.
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