Does Model Diversity Actually Matter in an ML Ensemble?Why combining different models doesn't guarantee a better ensemble: correlated errors, complementarity, underfitting, and validation on the full system.#Machine Learning#Ensemble Learning#Stacking#Model Validation#ExperimentationIbexcodeAugust 20, 2026
Machine Learning and Human Learning: Surprisingly Similar ProblemsMemorization, generalization, evaluation, errors, and redundant knowledge: what certain Machine Learning problems reveal about learning itself.#Machine Learning#Model Validation#Ensemble Learning#ExperimentationIbexcodeAugust 18, 2026
From Notebook to Production: Building a Robust ML Pipeline with Airflow and CI/CDFrom notebook to production: orchestrating an ML pipeline with Apache Airflow, data validation, CI/CD, deployment, and observability.#DevOps#MLOps#Machine Learning#Backend#Data EngineeringIbexcodeAugust 15, 2026
When an ML Improvement Doesn't Survive ValidationWhy an apparent ML gain can disappear under more rigorous validation: statistical uncertainty, selection bias, redundancy, and system-level impact.#Machine Learning#Model Validation#Experimentation#MLOps#Data LeakageIbexcodeAugust 4, 2026
Building a Temporal ML Ranking System Without Data LeakageHow to avoid data leakage in a temporal ML ranking system: future-only validation, OOF predictions, stacking, and calibration.#Machine Learning#MLOps#Data Leakage#Ranking#Backend#StackingIbexcodeJuly 12, 2026
Building an End-to-End ML Product as a Solo Developer: From Data to Mobile AppA firsthand account of building a complete ML product: data engineering, ranking, MLOps, cloud backend, CI/CD, and a Flutter app.#Machine Learning#MLOps#DevOps#Backend#Ranking#Data EngineeringIbexcodeJuly 1, 2026
