This lesson develops practical capability in Training Pipelines, Feature Workflows, Tracking, Model Registry, and Reproducibility as part of Cloud-Native MLOps with Azure ML and MLflow. Learners connect the concepts to a realistic workplace objective, perform guided work, validate evidence, and document improvements.
The lesson begins by defining the architecture, stakeholders, information flow, operating assumptions, and quality expectations that shape training pipelines, feature workflows, tracking, model registry, and reproducibility. It distinguishes essential decisions from optional enhancements and explains how security, governance, usability, cost, and supportability influence the final design.
Learners then map the end-to-end workflow, identify control points, and choose appropriate configuration or analysis steps. Special attention is given to dependencies, permissions, data boundaries, exception handling, documentation, and the evidence required for an accountable operational handoff.
Apply the lesson to a scenario in which an organization must improve implementing cloud-native MLOps with Azure Machine Learning and MLflow through workspaces, data, experiments, training pipelines, tracking, registries, deployment, CI/CD, infrastructure automation, monitoring, governance, security, drift detection, and retraining. Produce a concise plan, implementation or analysis evidence, a validation checklist, a troubleshooting record, and a prioritized recommendation for the next iteration.