End-to-End ML Pipeline with Feature Store

ML System Design hard ~50 hours

Build a complete ML pipeline: feature engineering with a feature store, model training with experiment tracking, A/B test deployment, and monitoring with drift detection.

Skills Demonstrated

Feature store (Feast/Tecton) Experiment tracking (MLflow/W&B) Model serving with latency SLAs Data drift detection

Implementation Steps

Set up Feast feature store with online + offline stores
Implement feature pipelines with point-in-time joins
Train model with MLflow experiment tracking
Build FastAPI model server with feature store integration
Add Evidently AI for data/model drift detection
Implement shadow deployment for safe model rollouts
Create monitoring dashboard with latency percentiles

Interview Relevance

Why this project matters for interviews ML system design interviews at FAANG test exactly this: can you design the infrastructure around the model? Feature stores, serving, monitoring, and A/B testing are the core topics.