Build a low-latency Ad Serving Pipeline
Last updated: February 7, 2026
Quick Overview
Design a low-latency ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Uber
February 7, 202637
6
3,597 solved
Design a low-latency ad serving system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Uber asks this during the Onsite 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
- Design the full ML lifecycle from data collection to model monitoring
- Address cold start, exploration/exploitation, and model freshness
- Discuss multi-objective optimization and ranking systems
- Plan for model debugging, fairness, and bias mitigation
- Design the feature store and training pipeline for scale
- Address model versioning, canary deployments, and rollback strategies
- Discuss the data flywheel and long-term system evolution
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 ensure fairness and reduce bias in the model?
- What would you do if model performance degrades over time?
- How would you handle a 10x increase in prediction requests?
- How would you handle the cold start problem?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For low-latency Ad Serving Pipeline, key functional requirements include:...
Capacity Estimation
Estimate the scale to drive design decisions. Assume 100M DAU with an average of 10 actions per user per day = 1B requests/day ~ 12K QPS average, ~36K...
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