DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning — Lesson 4: Monitoring, Troubleshooting, and Lifecycle Improvement

Learning Objectives

• Monitor endpoint health, logs, performance, data quality, and operational signals
• Troubleshoot environment, dependency, scoring, authentication, and deployment failures
• Document retraining, versioning, rollback, ownership, and improvement actions

Detailed Topics

• Monitor endpoint health: Monitor endpoint health, logs, performance, data quality, and operational signals
• Troubleshoot environment: Troubleshoot environment, dependency, scoring, authentication, and deployment failures
• Document retraining: Document retraining, versioning, rollback, ownership, and improvement actions

Guided Demonstration

1. Review the requirement, environment, dependencies, roles, risks, and expected outcome.
2. Demonstrate the relevant configuration, analysis, code, or administration workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, or test evidence.
4. Compare the observed result with the stated acceptance criteria.

Hands-On Activity

Complete a model-lifecycle review and create a monitoring and retraining checklist.

• Document assumptions and the planned sequence before making changes.
• Complete the activity and preserve evidence of normal and exception behavior.
• Record issues, corrective actions, and reusable operational or development notes.

Troubleshooting Focus

• Separate symptoms from causes and collect evidence before applying a fix.
• Check configuration, permissions, data, dependencies, capacity, connectivity, and recent changes.
• Validate rollback, recovery, support ownership, and user or business impact.

Validation Checklist

• The requirement and expected outcome are clear.
• The selected configuration, design, query, code, or process is documented.
• Normal, boundary, failure, security, and operational conditions are reviewed.
• The result is supported by evidence and the next improvement is prioritized.

Lesson Outcome

Learners can explain and apply Monitoring, Troubleshooting, and Lifecycle Improvement in a realistic scenario and validate the result with documented evidence.