Introduction to the Azure Machine Learning SDK
Azure Machine Learning workspaces
Exercise – Create a workspace
Azure Machine Learning tools and interfaces
Azure Machine Learning experiments
Exercise – Run experiments
Train a machine learning model with Azure Machine Learning
Exercise – Training and registering a model
Work with Data in Azure Machine Learning
Introduction to datastores
Exercise – Work with data
Work with Compute in Azure Machine Learning
Introduction to environments
Introduction to compute targets
Exercise – Work with Compute Contexts
Orchestrate machine learning with pipelines
Introduction to pipelines
Pass data between pipeline steps
Exercise – Create a pipeline
Deploy real-time machine learning services with Azure Machine Learning
Deploying a model as a real-time service
Consuming a real-time inferencing service
Troubleshooting service deployment
Exercise – Deploying a model as a real-time service
Deploy batch inference pipelines with Azure Machine Learning
Creating a batch inference pipeline
Publishing a batch inference pipeline
Exercise – Create a batch inference pipeline
Tune hyperparameters with Azure Machine Learning
Configuring early termination
Running a hyperparameter tuning experiment
Exercise – Tune hyperparameters
Automate machine learning model selection with Azure Machine Learning
Automated machine learning tasks and algorithms
Preprocessing and featurization
Running automated machine learning experiments
Exercise – Using automated machine learning
Explain machine learning models with Azure Machine Learning
Exercise – Interpret models
Detect and mitigate unfairness in models with Azure Machine Learning
Analyze model fairness with Fairlearn
Mitigate unfairness with Fairlearn
Exercise – Use Fairlearn with Azure Machine Learning
Monitor models with Azure Machine Learning
Enable Application Insights
Capture and view telemetry
Exercise – Monitor a model
Monitor data drift with Azure Machine Learning
Creating a data drift monitor
Exercise – Monitor data drift