ML system design fundamentals

🤖Key Takeaways

1
ML systems have TWO pipelines: training (offline, batch) and serving (online, real-time)
2
Feature stores centralize feature engineering: compute once, serve consistently to training and inference
3
Model serving: batch (pre-compute), online (request-time), near-real-time (streaming)
4
Monitoring ML models: data drift, concept drift, feature skew between training and serving

ML in Production is a System Design Problem

Building an ML model is 5% of the work. The other 95% is system design: data pipelines, feature engineering, training infrastructure, model serving, monitoring, and retraining. ML system design interviews test this full picture, not just model accuracy.

Data Collection & Processing

Ingest raw data from databases, event streams, and external sources. Clean, validate, and transform. Store in a data lake or feature store.

Advantages

•Feature stores eliminate training-serving skew
•Monitoring catches model degradation early
•MLOps practices bring software engineering rigor to ML

Disadvantages

•ML infrastructure is complex and expensive
•Data quality issues are the #1 cause of ML failures
•Retraining pipelines add operational overhead

🧪 Test Your Understanding

Knowledge Check1/1

What is training-serving skew?