ml-system-design-interview

Structure an ML system design interview using a 7-stage framework covering requirements, metrics, data, features, model, serving, and monitoring to articulate design choices for a recommendation system effectively.

Practice ML architecture whiteboarding for L6+ rounds by simulating a 45-minute design interview, focusing on inference pipelines, RAG, feature stores, and serving tradeoff analysis to prepare for senior engineering assessments.

A feature store fits into an ML inference pipeline by managing and serving features during the data and serving stages of the 7-stage design framework, ensuring consistent feature computation across training and production environments.

Yes, you can simulate a 45-minute RAG pipeline design round by practicing end-to-end ML system design scenarios, covering requirements, serving tradeoffs, and monitoring specific to retrieval-augmented generation architectures.

Analyze serving tradeoffs during an ML system design interview by evaluating latency, throughput, and cost across inference pipelines, ensuring your architecture meets the metrics defined in the initial requirements stage.

Design monitoring for an ML system architecture during the final stage of the 7-stage framework to track model performance, detect data drift, and ensure the recommendation system or RAG pipeline maintains production reliability.