Why ML Models Fail in Production: The 'Train and Forget' Trap Explained

Machine learning models that perform well during development frequently underperform or fail entirely once deployed in real-world environments. A key reason is the 'Train and Forget' mindset, which treats deployment as the finish line rather than the starting point of a model's lifecycle. Common failure causes include data drift, concept drift, training-serving skew, inconsistent feature stores, and inadequate infrastructure under production load. Many teams also lack proper model monitoring, tracking only system uptime while ignoring whether predictions remain accurate over time. Organizational pressures, resource constraints, and a tendency to treat ML like traditional software further compound the problem.