Explain how you will detect Concept Drift (the statistical properties of the target variable change over time) or Covariate Shift (the distribution of input features changes).

Never talk about optimizing a loss function without explaining how that optimization boosts user retention, conversion rates, or revenue.

: Implement tracking for data drift, error rates, and automated retraining triggers.

The book standardizes how to tackle open-ended ML design problems using these sequential steps: and define the business problem.

(Feast/Hopsworks) to serve low-latency online features; Data Lake (S3/Hudi) for historical training logs; NoSQL Cache (Redis) for real-time inference retrieval. Key Mistakes to Avoid in an ML System Design Interview