Machine Learning in Lyft's Business Context Questions
Application of machine learning engineering practices to Lyft's business problems, including demand forecasting, rider and driver matching, dynamic pricing, routing optimization, fraud detection, experimentation, ML productization, monitoring, and responsible AI within the ride-hailing domain.
EasyTechnical
0 practiced
Describe a production design for a feature store that serves low-latency online features for Lyft's matching and pricing models. Include choices for online store, offline store, feature materialization (precompute vs compute-on-read), consistency guarantees, TTL policies, and how to handle versioning of features.
EasyTechnical
0 practiced
Outline the end-to-end ML lifecycle you would follow to take a prototype rider ETA model to production at Lyft. Include specific steps for data validation, feature engineering, experiments, model training, CI/CD, model registry, deployment strategies (canary/blue-green), monitoring, alerting, and rollback mechanisms.
MediumTechnical
0 practiced
Describe an approach to estimate the causal impact of a pricing change on driver retention at Lyft. Discuss an identification strategy among randomized rollout, difference-in-differences, and instrumental variables; list required data, potential confounders, and how you'd check robustness of your estimates.
MediumTechnical
0 practiced
Write pseudocode or Spark Structured Streaming code to compute per-driver rolling metrics: active_minutes_last_30m and rides_completed_last_30m. Input stream events: (driver_id, event_type where event_type in ['available', 'unavailable', 'ride_complete'], event_ts). Update every minute and output to an online sink.
HardTechnical
0 practiced
Design a scalable system to produce interpretability explanations (for example SHAP) for a ranking/matching model and expose them to product and ops teams. Include computation strategy (precompute vs compute-on-request), storage schema for explanations, latency SLOs, privacy considerations, and UI UX guidelines for concise, actionable explanations.
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