Build a scalable Recommendation Pipeline
Last updated: September 29, 2025
Quick Overview
Design a scalable recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Shopify
September 29, 2025123
13
3,781 solved
Design a scalable recommendation system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Shopify asks this during the System Design Round to assess your understanding of the full ML lifecycle. They want to see how you translate a business problem into an ML objective, design the feature pipeline, and plan for model monitoring and retraining.
What the Interviewer Expects
- Map the business problem to a concrete ML objective
- Propose reasonable features and a baseline model
- Discuss basic model evaluation metrics
- Outline a simple serving architecture
Key Topics to Cover
How to Approach This
- Start by clarifying functional and non-functional requirements with the interviewer.
- Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
- Draw a high-level architecture first, then deep dive into 1-2 critical components.
- Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
- Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
- How would you handle a 10x increase in prediction requests?
- How would you run A/B tests on different model versions?
- What is your model retraining strategy?
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Requirements
Functional Requirements
- Real-time Recommendations: The system should provide personalized product recommendations to users based on their browsing history, purchase history, and similar use...
Capacity Estimation
Assuming Shopify has around 1 million active merchants and an average of 1000 visitors per merchant per day:
- Total daily visitors: 1 million merchants * 1000 visitors = 1 billion visitors per da...
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