Multi-Faceted Modeling Approach Questions
Modeling strategies that integrate multiple perspectives, modalities, or objectives to build more robust predictive systems. Covers ensemble methods, multi-task learning, multimodal data fusion, and orchestration of heterogeneous models within production ML pipelines.
EasyTechnical
0 practiced
Discuss ensembling models trained on different feature subsets versus ensembling models trained on the same features but with different algorithms. When is feature-level diversity particularly beneficial and how would you design such an ensemble in a production setting?
EasyTechnical
0 practiced
How can ensembling improve uncertainty estimation? Compare practical approaches: bootstrap ensembles, deep ensembles, Bayesian model averaging, and Monte Carlo dropout. Which approach would you use for out-of-distribution detection in production and why?
MediumTechnical
0 practiced
How would you compute SHAP or feature contribution explanations for an ensemble composed of XGBoost trees and a neural network? Discuss approximation strategies to keep compute tractable, runtime trade-offs, and how to present combined explanations for stakeholders.
EasyTechnical
0 practiced
Describe multi-task learning versus training separate single-task models. For a product that must predict user churn (binary classification) and next-purchase amount (regression), explain pros/cons, data labeling implications, and criteria you'd use to choose one approach in production.
MediumSystem Design
0 practiced
Design an end-to-end ML pipeline for a recommender using product text, product images, and user behavioral event streams. Requirements: 10k req/s, 200ms p95 latency, weekly retraining, graceful missing-modality handling, feature-store support, and offline/online feature parity. Sketch components for data ingestion, feature engineering, training, serving, caching, and CI/CD.
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