Design Image Processing Infrastructure for mobile apps
Last updated: February 13, 2026
Quick Overview
Design a event-driven image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Zscaler
System Design
Software Engineer
Zscaler
February 13, 2026Software Engineer
System Design Round
System Design
Hard
44
5
813 solved
Design a event-driven image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Zscaler goes beyond model selection. This System Design Round 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
- 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
Data collection and labeling strategy
Training pipeline and infrastructure
Model serving and latency optimization
A/B testing and experimentation
Feature engineering and feature stores
Online vs offline evaluation
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?
- How would you handle a 10x increase in prediction requests?
- How would you handle the cold start problem?
- What is your model retraining strategy?
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Requirements
Functional Requirements
- Image Upload: Mobile apps must allow users to upload images for processing.
- Image Processing: Support various image transformation operations (e.g., resiz...
Capacity Estimation
Back-of-Envelope Calculations
- Daily Requests: Assume 10 million image processing requests per day.
- Per-Second Throughput: 10 million requests / 86,400 seconds = ~116 requests per s...
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