Start by clarifying requirements and constraints (scale, latency, business metrics), then walk through the ML lifecycle from data to serving, emphasizing how each component addresses relevance, availability, diversity, and freshness. Use a whiteboard-friendly architecture diagram and discuss trade-offs at each stage, especially around cold-start and experimentation.
Pro tip: Anchor your design in measurable business outcomes (e.g., CTR, conversion, revenue) and explicitly tie each technical choice to one of the four ranking objectives. Show awareness of Shopify's multi-tenant, merchant-centric ecosystem by discussing how to handle heterogeneous catalogs and privacy.
Ask about scale (users, items, QPS), latency budget, and business goals. Define offline and online metrics (e.g., recall@k, NDCG, CTR, conversion, diversity, freshness) and how they map to the four ranking objectives.
Describe logging user interactions (views, clicks, purchases), item metadata, and real-time signals. Design batch and streaming pipelines for feature engineering, ensuring point-in-time correctness and low-latency feature serving.
Outline a multi-stage architecture: candidate generation (e.g., collaborative filtering, content-based, trending) followed by a ranking model (e.g., GBDT, DNN) that scores relevance. Include re-ranking for diversity and freshness, and discuss cold-start strategies like content-based fallbacks and exploration.
Explain the serving stack: feature store, model server, caching, and fallbacks. Address latency, scalability, and reliability (e.g., graceful degradation, circuit breakers, A/B testing infrastructure).
Detail how to run A/B tests, monitor model performance and data drift, and set up alerts. Discuss offline evaluation, online metrics, and continuous retraining.
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