#Mindmap — Azure ML Services mindmap focusing on Microsoft’s Azure DP-100 Exam

Azure Machine Learning: A Logical Learning Path

Workspace [High Priority]

The Workspace is the foundational container for all Azure ML resources. Start here to understand the basic structure.

Compute [High Priority]

Understand the various compute options:
- Compute Instances: For development and small-scale training
- Compute Clusters: For large-scale training and batch inference
- Inference Clusters: For production deployments
- Attached Compute: For integrating existing resources

Data [High Priority]

Learn how to manage data:
- Datastores: Connect to your storage services
- Datasets: Create and version your data

Experiments and Runs [High Priority]

Understand how to track your machine learning experiments:
- Runs: Individual executions of your code
- Metrics: Key performance indicators
- Logs: Detailed output for debugging

Models [High Priority]

Learn about model management:
- Model Registry: Central model storage
- Model Versioning: Track iterations of your models
- Model Deployment: Move models to production

Pipelines [High Priority]

Understand how to create reproducible workflows:
- Pipeline Steps: Individual units of work
- Pipeline Runs: Executions of entire workflows
- Pipeline Endpoints: Reusable pipeline configurations

Automated ML [High Priority]

Explore automated machine learning capabilities:
- Task Types: Classification, Regression, Time Series Forecasting
- Model Explainability: Understand model decisions

Designer [Medium Priority]

Learn about the drag-and-drop interface for creating ML pipelines:
- Prebuilt Modules: Ready-to-use components
- Custom Modules: Create your own reusable components

Notebooks [Medium Priority]

Understand how to use Jupyter notebooks in Azure ML for interactive development.

MLOps [Medium Priority]

Learn about integrating ML workflows with DevOps practices:
- Azure DevOps Integration
- CI/CD Pipelines

Responsible AI [Medium Priority]

Explore tools for ethical AI development:
- Fairness: Ensure your models treat all groups equitably
- Explainability: Understand and interpret model decisions
- Privacy & Security: Protect sensitive data and models

Security & Governance [Medium Priority]

Learn about securing your ML resources:
- Role-Based Access Control (RBAC)
- Azure Active Directory
- Virtual Networks

Integration [Lower Priority]

Explore how Azure ML integrates with other Azure services:
- Azure Synapse Analytics
- Azure Databricks
- Power BI
- Azure IoT

By following this order, you’ll build a strong foundation in Azure ML, starting with the core components and gradually moving to more advanced and specialized features. This approach allows you to understand how the pieces fit together before diving into higher-level services and integrations.