ML System Design Framework

In this segment, we’re introduced to a powerful 5-part framework for approaching ML system design interviews. This structured method ensures we address both technical depth and strategic thinking—key traits interviewers look for in strong candidates.

• 1. Problem Navigation: We start by asking clarifying questions to understand functional and non-functional requirements, product goals, infrastructure constraints, and user experience. We define success metrics, state our assumptions, and frame the scope clearly.
• 2. High-Level Design: We map business goals to ML objectives, walk through core system components (data ingestion, processing, modeling, inference, monitoring), and identify what needs to run in real-time versus batch.
• 3. Data Strategy & Feature Engineering: We explain how data is collected and labeled, ensure data quality, and outline our approach to creating, storing, and updating features—including how we handle feature freshness and online/offline processing.
• 4. Modeling Approach: We present specific model choices with trade-offs in interpretability, scalability, and accuracy. We detail training methods (batch, online, hybrid), address data distribution shifts, and outline infrastructure needs.
• 5. Evaluation, Deployment & Monitoring: We describe offline evaluation strategies, deployment infrastructure (latency, model size, caching), and our plan for monitoring model health (drift, degradation) while tying results back to business KPIs.

Using this framework helps us think like system-level ML engineers, ensuring we cover everything from high-level goals to production-readiness with clarity and confidence.

If you want to learn even more from Yayun:

• Book 1 on 1 career guidance with her here: https://calendly.com/your-friend-yayun/1on1
• Follow her on LinkedIn: https://www.linkedin.com/in/yayunjin/