ML system design is highly conversational. Book mock sessions with peers or use interview platforms to practice articulating your trade-offs clearly within a strict 45-minute limit.
Most candidates fail ML interviews because they focus too much on model architecture (like Transformers or ResNet) and forget about the system: data pipelines, serving infrastructure, and monitoring. The 7-Step ML System Design Framework machine learning system design interview alex xu pdf github
Techniques like SMOTE, downsampling the majority class, or adjusting loss functions (Focal Loss). ML system design is highly conversational
: Ask clarifying questions to understand the business goal (e.g., maximize clicks vs. revenue), scale (DAU, data volume), and latency constraints. Problem Framing downsampling the majority class
Explicitly separate offline metrics (ROC-AUC, F1-score, Log Loss) from online business metrics (Click-Through Rate, Revenue Lift, Conversion Rate). 4. Post-Deployment, Monitoring, and Scale