ML CI/CD

A machine-learning release depends on code, data, feature definitions, configuration and trained parameters. ML CI/CD coordinates these artifacts and their checks, rather than treating a model file as an ordinary code build. Continuous training may create candidates when new data or another trigger arrives, but it remains distinct from automatically deploying them. Data validation checks whether training should proceed; model validation checks whether the candidate satisfies performance and compatibility requirements. General CI/CD delivers software changes, while ML CI/CD additionally manages the lineage and behavior of learned artifacts whose outcomes depend on changing input distributions.

The practitioner builds modular pipeline components, pins data and environment references and records artifact lineage. Gates should test data quality, leakage, relevant subgroups and the serving contract. A useful deliverable includes pipeline definitions, candidate evaluation records and a registered model version. Promotion should preserve the exact accepted artifact. Online rollout and monitoring complement offline checks, while rollback includes preprocessing and feature dependencies so the restored model receives inputs consistent with its training and validation.

Automated retraining can reproduce bad data or silently promote a regression if the gates are weak. Aggregate metrics may conceal failures in important segments. Feature-store or preprocessing changes can break a model without changing its weights. Quality requires lineage, task-relevant validation and controlled promotion. A scheduled pipeline is not continuous improvement by itself: each candidate must earn acceptance under a stable comparison, and the system must handle cases where no new model should be released.