Set up and manage Azure ML workspaces for experimentation and collaboration.
Lessons:

• Workspace creation and configuration
• Compute targets and environments
• Azure ML studio and SDK overview
  
  
• Key Topics:
• Workspace architecture
• Compute management
• Environment reproducibility

Run experiments and manage datasets using Azure ML tools.
Lessons:

• Dataset registration and labeling
• Experiment tracking
• Using notebooks and scripts
  
  
• Key Topics:
• Data preparation
• Experiment lifecycle
• MLflow integratio

Train models using pipelines, AutoML, and hyperparameter tuning.
Lessons:

• Pipeline creation and orchestration
• AutoML configuration
• Model evaluation and tuning
  
  
• Key Topics:
• Training automation
• Performance optimization
• Model selection

 Register, version, and evaluate models for deployment readiness.

Lessons:

• Model registry and metadata
• Responsible AI tools
• Model explainability
  
  
• Key Topics:
• Model governance
• Fairness and transparency
• Evaluation metrics

Deploy models to endpoints and monitor performance in production.
Lessons:

• Real-time and batch deployment
• Endpoint configuration
• Monitoring and retraining
  
  
• Key Topics:
• Deployment strategies
• Model consumption
• Lifecycle management