Build a scalable Image Processing Pipeline
Last updated: March 28, 2026
Quick Overview
Design a scalable image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Morgan Stanley
March 28, 202629
7
3,921 solved
Design a scalable image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Morgan Stanley 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 debug a model that works well offline but poorly online?
- What is your model retraining strategy?
- What would you do if model performance degrades over time?
- How would you run A/B tests on different model versions?
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Requirements Clarification
Before diving into the architecture, clarify the scope with the interviewer. For scalable Image Processing Pipeline, key functional requirements inclu...
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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