Continuous Training
Automatically retraining ML models when new data arrives or performance degrades below a threshold.
Overview
Continuous training (CT) is an MLOps practice where ML models are automatically retrained on fresh data on a schedule or when triggered by events like data drift detection or performance degradation. It extends CI/CD practices to the ML lifecycle.
Key Details
A continuous training pipeline typically includes data validation, automated feature engineering, model training, evaluation against baselines, and automated deployment (if the new model passes quality gates). This ensures models stay current with evolving data patterns. CT is essential for applications where data distributions change frequently, such as recommendation systems, fraud detection, and demand forecasting.