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Build an end-to-end ML pipeline

AmazonApplied Scientist · Onsite
HardApplied ScientistOnsiteML System Design

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Build an end-to-end ML pipeline evaluates ML product requirements, data/labeling, modeling, serving architecture, evaluation, monitoring, and trade-offs in a realistic interview setting. A strong answer states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.

Given a CSV with shipment events (order_id, origin, destination, ship_date, promised_date, carrier, weight, item_count, scan_events[], delivered_date), build from scratch a Python pipeline that: (

  1. loads and validates data; handles missing values, outliers, and time zones; (
  2. creates features (e.g., day-of-week, route, carrier stats, dwell times from scan_events); (
  3. labels examples as delayed if delivered_date − promised_date > 48 hours (justify how you handle undelivered items and censoring); (
  4. trains a baseline model (logistic regression or gradient-boosted trees) with cross-validation; reports ROC-AUC and PR-AUC; addresses class imbalance; (
  5. calibrates probabilities and explains top features; (
  6. outputs a CSV of top-K at-risk shipments with calibrated probabilities and reason codes. Optimize for runtime < 5 minutes on 1M rows and memory < 4 GB, and discuss strategies to speed up training/inference and ensure reproducibility.