Release models through traceable artifacts, feature parity, promotion gates, staged rollout, monitoring and rollback.
Learning path
- Training manifests: link data, code, configuration and artifact
- Training-serving parity: compare feature values at one prediction clock
- Model promotion: require evidence before changing the serving pointer
- Shadow and canary rollout: compare a candidate without losing a rollback
- Model monitoring: separate input drift, data faults and delayed outcomes
- Retraining decisions: require a reason and a challenger comparison
- Inference logs: keep diagnostic joins without copying sensitive payloads
- Project: release receipt triage with lineage, canary checks and rollback
Connected foundations
Use the earlier data and model boundaries as prerequisites.
Practice
Continue into another subject: Computer Vision Tutorial.
Continue into another subject: Feature Stores Tutorial.
Continue into another subject: Privacy-Aware ML Tutorial.