This blog post explores the critical process of building ML model retraining systems. It covers the importance of continuous model updates, key architectural components, and best practices for implementation. The post includes practical Python code examples for data pipeline setup, model evaluation, and automated retraining processes. It also discusses challenges, optimization strategies, and future trends in ML model maintenance.