Explain your ML project end-to-end
Overview
This question evaluates a data scientist's end-to-end machine learning competencies, including problem framing and metric justification, data sourcing and labeling, model selection and calibration, class-imbalance handling, deployment and monitoring, experimentation design, and post-mortem analysis; it is in the Machine Learning domain and tests both conceptual understanding and practical application across modeling and MLOps. It is commonly asked to assess an interviewee's ability to justify trade-offs, reason about operational constraints such as latency, fairness and cost, design valid evaluation and A/B testing strategies, and define measurable monitoring and rollback criteria.
End-to-End ML Project Deep Dive (7 Parts)
Assume you are describing the most complex ML project on your resume. Answer each part precisely and concretely.
- Business Objective, Target, Constraints, and Metrics
- Define: business objective, target variable, key constraints (e.g., latency/SLA, fairness, cost), and the primary success metric (justify PR-AUC vs. ROC-AUC vs. cost-weighted error).
- Data and Labeling
- Describe data sources and the labeling strategy.
- Explain train/validation/test splits; if temporal, use a time-based split.
- Detail how you prevented leakage with concrete examples you checked for.
- Model Selection and Evidence
- List candidate models and the exact hyperparameters you tuned.
- Provide an ablation plan that isolates the marginal value of two specific feature groups.
- Explain a bias–variance trade-off decision you made and the evidence.
- Class Imbalance and Thresholding
- Explain your resampling or weighting strategy.
- Explain how you set decision thresholds.
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Compute the following scenario:
- Validation set size: 10,000 with 8% positives.
- Baseline at threshold 0.50: precision = 0.70, recall = 0.45.
- After adding Feature Set X and doing probability calibration, at threshold 0.30: precision = 0.58, recall = 0.66.
- Compute F1 for both, expected TP, FP, FN at each threshold, and decide which to deploy if FP costs 1 and FN costs 5. Show your cost calculation.
- Deployment and Monitoring
- Propose monitoring metrics (at least: calibration, drift on three top features, alert thresholds).
- Define a retraining trigger rule.
- Explain how you’ll guard against data pipeline schema changes.
- Online Validation (Experimentation)
- Design an A/B test with guardrail metrics.
- Provide a sample-size/duration estimate.
- Give a rollback plan if long-tail segments regress.
- Post-Mortem Readiness
- Name two plausible failure modes.
- Explain how you would debug them using specific offline error buckets and online slices.
Solution
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