Common Machine Learning Pitfalls and Debugging Questions

Knowledge of frequent failure modes in machine learning projects and practical approaches to detect and resolve them. Topics include data leakage, distribution shift, class imbalance and label noise, non stationary data, reproducibility failures, metric misspecification, overfitting, and systematic debugging strategies such as targeted experiments, ablation studies, unit tests for data pipelines, experiment tracking, and production monitoring.