Car Recommendation Architecture

You work as a data scientist at Cars.com. The company is launching a new personalized car recommendation engine that must support both real-time and batch inference, adapt to changing user preferences, and enable frequent model updates via CI/CD pipelines.

How would you design the end-to-end architecture for this system, ensuring it can robustly detect and respond to data distribution shifts while maintaining seamless CI/CD for machine learning models?