From Kaggle Notebooks to Production: ML Engineering in 2026

Most ML tutorials ignore the production challenges that real engineers face daily—model drift, data integrity, and monitoring. This project collection bridges that gap by teaching the skills that hiring managers at product companies actually value: serving models safely, detecting distribution shifts, and preventing data leakage. These aren't Kaggle problems; they're the engineering realities that separate ML practitioners from notebook experimenters.