DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning — Lesson 3: Model Registration, Deployment, and Endpoint Testing

Learning Objectives

• Register model assets and define deployment environment, scoring code, and configuration
• Deploy the model to a managed endpoint with appropriate compute and authentication
• Test requests, responses, latency, failures, permissions, and endpoint behavior

Detailed Topics

• Register model assets and define deployment environment: Register model assets and define deployment environment, scoring code, and configuration
• Deploy the model to a managed endpoint with appropriate compute and authentication: Deploy the model to a managed endpoint with appropriate compute and authentication
• Test requests: Test requests, responses, latency, failures, permissions, and endpoint behavior

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

Deploy a candidate model and validate the endpoint with normal and error test cases.

• 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 Model Registration, Deployment, and Endpoint Testing in a realistic scenario and validate the result with documented evidence.