DP-3007: Train and Deploy a Machine Learning Model with Azure Machine Learning — Lesson 1: Azure Machine Learning Workspace and Data Preparation

Learning Objectives

• Explain Azure Machine Learning workspaces, compute, data assets, environments, jobs, and identity
• Connect, inspect, clean, transform, and split data for a supervised-learning task
• Create reproducible training inputs, scripts, dependencies, and experiment structure

Detailed Topics

• Explain Azure Machine Learning workspaces: Explain Azure Machine Learning workspaces, compute, data assets, environments, jobs, and identity
• Connect: Connect, inspect, clean, transform, and split data for a supervised-learning task
• Create reproducible training inputs: Create reproducible training inputs, scripts, dependencies, and experiment structure

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

Prepare a dataset and configure a reproducible Azure Machine Learning experiment.

• 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 Azure Machine Learning Workspace and Data Preparation in a realistic scenario and validate the result with documented evidence.