Design a Image Processing Service
Last updated: March 28, 2026
Quick Overview
Design a high-throughput image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Brex
March 28, 2026416
6
3,667 solved
Design a high-throughput image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from Brex's Onsite tests your ability to think about ML systems at scale. The interviewer expects discussion of data quality, feature stores, model serving infrastructure, and A/B testing strategy.
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
- What is your model retraining strategy?
- How would you ensure fairness and reduce bias in the model?
- What would you do if model performance degrades over time?
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Capacity Estimation
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