Retrains

Retrains, in the context of machine learning, refers to the process of updating an existing model by training it again on data. This can involve new labeled examples, revised objectives, or both. Retraining is used in systems that must adapt to changing data, user behavior, or regulatory requirements.

Rationale: Over time, data drift and concept drift can cause a model's predictions to become less accurate. Retraining can refresh the model's knowledge, incorporate new features, and reduce bias, while preserving or improving performance.

Approaches: Batch retraining reuses historical data and new data at set intervals. Online or incremental retraining updates model parameters as new data arrives. Rolling-window strategies keep a fixed-size recent dataset. Transfer learning or fine-tuning can adapt a model to a related task or domain with limited new data.

Data and deployment considerations: Retraining requires access to labeled data, data quality checks, and feature engineering pipelines. Data versioning and reproducible training pipelines are essential. Retrained models must be tested in a staging environment; deployment should support versioning and rollback.

Evaluation and monitoring: Retraining plans are guided by performance metrics, drift detectors, and business triggers. Before replacing a production model, teams may use holdout sets, backtesting, A/B tests, or canary deployments to compare new and existing models.

Governance and best practices: Define retraining cadence or criteria, maintain audit logs, and separate training from inference environments. Use governance practices to manage data privacy, model cards, and explainability. The term retrains describes the action; retraining denotes the process as a whole.