Automated Retraining
Automated Retraining is a machine learning (ML) and artificial intelligence (AI) methodology that involves automatically updating and retraining models with new data to maintain or improve their performance over time. It typically includes pipelines for data collection, model evaluation, triggering retraining based on performance metrics or schedule, and deployment of updated models. This approach helps address issues like model drift, where a model's predictions become less accurate as real-world data changes.
Developers should learn and use Automated Retraining when building production ML systems that require continuous adaptation to evolving data, such as in recommendation engines, fraud detection, or natural language processing applications. It ensures models remain relevant and accurate without manual intervention, reducing maintenance overhead and improving reliability in dynamic environments like e-commerce or financial services.