Build a real-time Image Processing Pipeline
Last updated: June 7, 2026
Quick Overview
Design a real-time image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Neon
June 7, 2026149
6
1,303 solved
Design a real-time image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from Neon's Technical Screen 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 would you do if model performance degrades over time?
- How would you run A/B tests on different model versions?
- How would you debug a model that works well offline but poorly online?
- How would you ensure fairness and reduce bias in the model?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For real-time Image Processing Pipeline, key functional requirements incl...
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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