Module 1: Introduction to Azure Machine Learning

• Getting Started with Azure Machine Learning
• Azure Machine Learning Tools
• Lab: Creating an Azure Machine Learning Workspace
• Lab: Working with Azure Machine Learning Tools

Module 2: No-Code Machine Learning with Designer

• Training Models with Designer
• Publishing Models with Designer
• Lab: Creating a Training Pipeline with the Azure ML Designer
• Lab: Deploying a Service with the Azure ML Designer

Module 3: Running Experiments and Training Models

• Introduction to Experiments
• Training and Registering Models
• Lab: Running Experiments
• Lab: Training and Registering Models

Module 4: Working with Data

• Working with Datastores
• Working with Datasets
• Lab: Working with Datastores
• Lab: Working with Datasets

Module 5: Compute Contexts

• Working with Environments
• Working with Compute Targets
• Lab: Working with Environments
• Lab: Working with Compute Targets

Module 6: Orchestrating Operations with Pipelines

• Introduction to Pipelines
• Publishing and Running Pipelines
• Lab: Creating a Pipeline
• Lab: Publishing a Pipeline

Module 7: Deploying and Consuming Models

• Real-time Inferencing
• Batch Inferencing
• Lab: Creating a Real-time Inferencing Service
• Lab: Creating a Batch Inferencing Service

Module 8: Training Optimal Models

• Hyperparameter Tuning
• Automated Machine Learning
• Lab: Tuning Hyperparameters
• Lab: Using Automated Machine Learning

Module 9: Interpreting Models

• Introduction to Model Interpretation
• Using Model Explainers
• Lab: Reviewing Automated Machine Learning Explanations
• Lab: Interpreting Models

Module 10: Monitoring Models

• Monitoring Models with Application Insights
• Monitoring Data Drift
• Lab: Monitoring a Model with Application Insights
• Lab: Monitoring Data Drift