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
