Design a Image Processing Service

Last updated: November 22, 2025

Quick Overview

Design a real-time image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

Cockroach Labs
System Design
Software Engineer
Cockroach Labs
November 22, 2025
Software Engineer
System Design Round
System Design
Hard

0

6

3,288 solved


Design a real-time image processing system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.

ML system design at Cockroach Labs 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
Feature engineering and feature stores
Training pipeline and infrastructure
ML objective formulation and metric selection
Monitoring and model degradation detection
Online vs offline evaluation
Feedback loops and model retraining
How to Approach This
  1. Start by clarifying functional and non-functional requirements with the interviewer.
  2. Estimate the scale: QPS, storage, bandwidth. This drives your design decisions.
  3. Draw a high-level architecture first, then deep dive into 1-2 critical components.
  4. Discuss trade-offs explicitly (e.g., consistency vs availability, SQL vs NoSQL).
  5. Address failure scenarios, monitoring, and how the system handles 10x traffic spikes.
Possible Follow-up Questions
  • How would you handle a 10x increase in prediction requests?
  • How would you ensure fairness and reduce bias in the model?
  • What is your model retraining strategy?
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Sample Answer
Requirements Clarification

Before diving into the architecture, clarify the scope with the interviewer. For Image Processing Service, key functional requirements include: what a...

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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