What is ML system design?
ML system design is architecting everything around the model: data collection, feature pipelines, training, serving, monitoring, and the feedback loop. The model is a small piece; the system that feeds, serves, and maintains it is the real engineering.
Why it matters
A great model with no pipeline, no monitoring, and no retraining path is a demo, not a product. ML system design is what makes models work in the real world over time, and it is the focus of ML engineering interviews at strong companies. It ties together everything in this track.
What to learn
- Framing the problem and success metrics
- The data and feature pipeline
- Training and retraining workflows
- Serving architecture and latency needs
- Monitoring and the feedback loop
- Handling scale and failure
- The interview structure for ML design
Common pitfall
Obsessing over model choice while ignoring data and serving. In interviews and in practice, fixating on which architecture to use — while hand-waving data collection, labeling, serving, and monitoring — signals inexperience. The model is often the easy part; the system around it is where real ML engineering and the hard decisions live.
Resources
Primary (free):
- Chip Huyen — ML systems design · docs
- Google — Rules of ML · docs
- Made With ML — Design · course
Practice
Take a prompt like "design a system to recommend articles" and work through it end to end: success metric, data and features, training and retraining, serving, and monitoring with a feedback loop. Spend most of your time outside the model. Done when the design covers the full lifecycle, not just the algorithm.
Outcomes
- Design the full pipeline around a model, not just the model.
- Frame the problem with clear success metrics.
- Plan training, serving, monitoring, and feedback.
- Structure an ML system design interview answer.