Apply the MLOps curriculum to a checked project.
Project brief
Required concepts
- 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
Completion standard
Submit runnable work, a data or run manifest, measured results and failure-case evidence.