What is continuous training (CT) in MLOps and how is it different from retraining pipelines?

Continuous training automatically retrains models as new data arrives, while retraining pipelines are usually scheduled or manual.

Continuous Training (CT) is an automated paradigm where model retraining is triggered by data drift, performance degradation, or new data availability. Unlike scheduled retraining, CT is event-driven and tightly integrated with monitoring systems. It requires robust validation gates, automated rollback mechanisms, and dataset versioning to prevent unstable model updates. CT is often paired with CI/CD to form CI/CT/CD pipelines in modern MLOps stacks.