Why Most Machine Learning Projects Fail: Five Critical Pitfalls Revealed in Industry Analysis
Comprehensive analysis identifies five recurring pitfalls driving 85% ML project failure rate: wrong problem selection, data quality issues, model-to-product gap, offline-online mismatch, and non-technical blockers, with actionable solutions for practitioners.