This is the kind of question where you nod confidently and then immediately realize you have six different topics to cover and maybe 45 minutes.
Start by clarifying requirements and constraints (e.g., latency, scale, personalization goals) to frame the design. Then walk through the end-to-end ML pipeline, highlighting key components and trade-offs at each stage. Emphasize scalability, real-time serving, and monitoring for production readiness.
Pro tip: Focus on the unique challenges of real-time recommendations, such as low-latency feature serving and online-offline consistency, and discuss how you'd handle cold-start and feedback loops.
Ask questions to understand the product: user scale, item catalog size, latency requirements, personalization goals, and update frequency. This ensures the design meets actual needs.
Outline how to collect user interactions (clicks, views, purchases) and item metadata in real-time and batch. Describe feature engineering: user features, item features, context features, and how to compute and store them for low-latency access.
Choose model architectures (e.g., matrix factorization, two-tower, deep learning) and training pipelines (batch/online). Discuss offline metrics (recall@k, NDCG) and how to validate before deployment.
Design a low-latency serving system: feature store for online features, model serving with caching, and fallback strategies. Address scalability with load balancing, sharding, and autoscaling.
Set up monitoring for model performance, data drift, and system health. Plan for A/B testing, online evaluation, and continuous retraining to adapt to changing user behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.