PROJECT BRIEF / 03
The lifecycle after training
Azure ML + MLflow workshop
The context
A model training notebook does not explain how a team will operate that model. This workshop brings the deployment, monitoring, and governance lifecycle into one learning path.
The approach
- Track training runs, metrics, artifacts, and model registration with MLflow.
- Explore batch endpoint deployment and automated retraining pipelines.
- Demonstrate drift and data-quality reporting.
- Introduce audit logging and governance with Azure-native services.
The leadership connection
Technical leadership that helps teams understand ownership and operational capability across the complete ML lifecycle.
What to look at
Walk the lifecycle
Follow the workshop modules from setup through governance.
Look beyond the model
Review the retraining, observability, and audit-report components.
Plan your operating model
Identify ownership, monitoring thresholds, and approval requirements before adapting the sample.
Continue exploring