Microsoft Fabric with MLOPS — Lesson 2: Experiment Tracking, Feature Engineering and Model Training

Learning Objectives

• Engineer features and train suitable models using notebooks, libraries, parameters, scalable processing, and repeatable code
• Track experiments, metrics, artifacts, models, lineage, versions, and approvals with MLflow-compatible practices
• Validate experiment tracking, feature engineering and model training decisions with documented requirements, test evidence, risk controls, and measurable acceptance criteria

Detailed Topics

• Engineer features and train suitable models using notebooks: Engineer features and train suitable models using notebooks, libraries, parameters, scalable processing, and repeatable code
• Track experiments: Track experiments, metrics, artifacts, models, lineage, versions, and approvals with MLflow-compatible practices
• Validate experiment tracking: Validate experiment tracking, feature engineering and model training 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 reporting 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

Build and compare documented model experiments.

• 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, calculations, 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 Experiment Tracking, Feature Engineering and Model Training in a realistic scenario and validate the result with documented evidence.