Microsoft Data Science Bootcamp for Freshers — Lesson 3: Model Evaluation, Azure ML and Deployment

Learning Objectives

• Use suitable metrics, validation, cross-validation, imbalance handling, tuning, interpretation, and error analysis
• Track experiments, register models, deploy endpoints, test services, monitor behavior, and manage versions
• Validate model evaluation, azure ml and deployment decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Detailed Topics

• Use suitable metrics: Use suitable metrics, validation, cross-validation, imbalance handling, tuning, interpretation, and error analysis
• Track experiments: Track experiments, register models, deploy endpoints, test services, monitor behavior, and manage versions
• Validate model evaluation: Validate model evaluation, azure ml and deployment decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Guided Demonstration

1. Review the requirement, users, data, dependencies, permissions, risks, and expected result.
2. Demonstrate the relevant design, configuration, development, analysis, administration, or support workflow step by step.
3. Capture settings, outputs, logs, screenshots, calculations, test results, and other useful evidence.
4. Compare the observed result with acceptance criteria and explain any corrective action.

Hands-On Activity

Deploy and evaluate a model using Microsoft machine-learning services.

• Document assumptions, prerequisites, and the planned sequence before starting.
• Complete the activity and preserve evidence of normal, boundary, and failure behavior.
• Record issues, corrective actions, reusable notes, and the next improvement priority.

Scenario Analysis

• Identify the business or technical decision that must be made.
• Compare valid implementation approaches and their trade-offs.
• Consider security, privacy, accessibility, performance, support, and operational impact.

Troubleshooting Focus

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

Validation Checklist

• The requirement, scope, inputs, and expected outcome are clear.
• The selected design or configuration is documented and follows approved standards.
• Normal, boundary, failure, security, accessibility, 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 Evaluation, Azure ML and Deployment in a realistic scenario and validate the result with documented evidence.