Design a Feature Flag for Scale AI
Last updated: December 19, 2025
Quick Overview
Design a multi-tenant feature flag system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Scale AI
December 19, 202546
6
4,596 solved
Design a multi-tenant feature flag system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
This ML system design question from Scale AI's Onsite 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
- 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
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 handle the cold start problem?
- How would you handle a 10x increase in prediction requests?
- What would you do if model performance degrades over time?
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