Design Image Processing Infrastructure for global users
Last updated: November 9, 2025
Quick Overview
Design a high-throughput image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Goldman Sachs
November 9, 20255
6
285 solved
Design a high-throughput image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Goldman Sachs goes beyond model selection. This Technical Screen question evaluates your ability to design end-to-end ML pipelines, from data collection to model serving, while considering production constraints like latency and reliability.
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 run A/B tests on different model versions?
- How would you handle the cold start problem?
- What is your model retraining strategy?
- What would you do if model performance degrades over time?
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Requirements
Functional Requirements
- Image Upload: Users can upload images through a web interface.
- Image Processing: The system must perform operations such as scaling, cropping, and applying fi...
Capacity Estimation
Assuming Goldman Sachs has an average of 5 million image uploads per day:
- Requests per second (RPS):
- 5 million images / 86400 seconds = ~58 RPS
- Peak Load: During peak hours (3 hours),...
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