ML System Design Interview Questions

Common ML system design patterns

• End-to-end ML pipeline design (data → features → model → serving)
• Feature store architecture and feature freshness
• Online vs batch prediction trade-offs
• Model versioning and rollback strategies
• A/B testing and shadow deployment for ML models
• Monitoring for data drift and model degradation

Common mistakes in ML system design

• Focusing only on the model and ignoring data pipelines
• Not discussing how to handle model failures in production
• Ignoring feature freshness requirements
• Designing overly complex systems for simple problems
• Not considering the feedback loop between model predictions and training data

How ML system design is evaluated

Related ML concepts

• Model evaluation interview questions
• LLM interview questions
• System design interview questions