Explain an ML project end-to-end with tradeoffs
Overview
This question evaluates a candidate's competency in end-to-end machine learning system design and delivery, covering problem framing, target definition and label leakage prevention, data and metric selection, feature engineering with privacy and fairness constraints, model choice trade-offs, hyperparameter and ablation analysis, and post-deployment monitoring and impact quantification. It is commonly asked to assess practical production experience and trade-off reasoning in the Machine Learning domain, testing both practical application and conceptual understanding of modeling, evaluation, and operational constraints.
Pick one of your production ML projects and walk through it end-to-end. Be specific: 1) Problem framing (prediction vs causal decisioning), target definition, and how you prevented label leakage; 2) Data sources, sampling window, and offline metric(s) with rationale (e.g., AUC vs calibration/Brier for monetization); 3) Feature engineering, handling sparse/categorical signals, and how you enforced privacy/fairness constraints; 4) Model choices and tradeoffs (e.g., XGBoost vs shallow nets vs GLM), hyperparameter strategy, and ablations you ran; 5) Error analysis and post-deployment monitoring (drift, stability, guardrail metrics); 6) How you translated model lifts into product impact without an A/B test (e.g., causal uplift modeling, CUPED, backtests); 7) What you would change on a v2 if given twice the data or stricter latency limits.