Design a Analytics Service
Last updated: April 17, 2026
Quick Overview
Design a low-latency analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
Scale AI
April 17, 202653
5
4,449 solved
Design a low-latency analytics system that handles millions of requests. Discuss trade-offs in consistency, availability, and performance.
ML system design at Scale AI 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
- Define clear ML objectives with appropriate loss functions and metrics
- Design a comprehensive feature engineering pipeline
- Discuss model selection with trade-offs (complexity vs interpretability vs latency)
- Plan online and offline evaluation strategies including A/B testing
- Address serving infrastructure: batch vs real-time, latency requirements
- Consider data quality, labeling strategy, and feedback loops
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 a 10x increase in prediction requests?
- How would you debug a model that works well offline but poorly online?
- What would you do if model performance degrades over time?
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Requirements
- Functional Requirements:
- Handle millions of concurrent requests with low-latency response times (under 100ms).
- Provide real-time analytics and insights from machine learning models...
Capacity Estimation
Assuming Scale AI receives approximately 10 million requests per day, we can estimate the following:
-
Requests per second (RPS):
10 million requests / 86400 seconds = ~115.7 RPS -
**Data...
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