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  wikipedia Automating model retraining in a production environment is a crucial aspect of Machine Learning Operations ( MLOps ). Here's a breakdown of how to achieve this: Triggering Retraining: There are two main approaches to trigger retraining: Schedule-based: Retraining happens at predefined intervals, like weekly or monthly. This is suitable for models where data patterns change slowly and predictability is important. Performance-based: A monitoring system tracks the model's performance metrics (accuracy, precision, etc. ) in production. If these metrics fall below a predefined threshold, retraining is triggered. This is ideal for models where data can change rapidly. Building the Retraining Pipeline: Version Control: Use a version control system (like Git) to manage your training code and model artifacts. This ensures reproducibility and allows easy rollbacks if needed. Containerization: Package your training code and dependencies in a container (like Docke...