Build and operate machine learning solutions with Azure Machine Learning

In this module, you will learn how to:

• Provision an Azure Machine Learning workspace.
• Use tools and interfaces to work with Azure Machine Learning.
• Run code-based experiments in an Azure Machine Learning workspace.

In this module, you will learn how to:

• Use a ScriptRunConfig to run a model training script as an Azure Machine Learning experiment.
• Create reusable, parameterized training scripts.
• Register trained models.

• Create and use datastores in an Azure Machine Learning workspace.
• Create and use datasets in an Azure Machine Learning workspace.

• Work with environments
• Work with compute targets

• Create Pipeline steps
• Pass data between steps
• Publish and run a pipeline
• Schedule a pipeline

In this module, you will learn how to:

• Deploy a model as a real-time inferencing service.
• Consume a real-time inferencing service.
• Troubleshoot service deployment

Learn how to create, publish, and use batch inference pipelines with Azure Machine Learning.

Learn how to use Azure Machine Learning hyperparameter tuning experiments to optimize model performance.

In this module, you will learn how to:

• Use Azure Machine Learning's automated machine learning capabilities to determine the best performing algorithm for your data.
• Use automated machine learning to preprocess data for training.
• Run an automated machine learning experiment.

After completing this module, you'll be able to:

• Articulate the problem of data privacy
• Describe how differential privacy works
• Configure parameters for differential privacy
• Perform differentially private data analysis

Learn how to explain models by calculating and interpreting feature importance.

In this module, you will learn:

• How to evaluate machine learning models for fairness.
• How to mitigate predictive disparity in a machine learning model.

Learn how to use Azure Application Insights to monitor a deployed Azure Machine Learning model.

Learn how to monitor data drift in Azure Machine Learning.

In this module, you will:

• Apply and understand Role-Based Access Control in Azure Machine Learning
• Describe how secrets are managed in Azure Machine Learning
• Use an Azure Machine Learning workspace with Azure Virtual Network

• Introduction
• Azure Machine Learning workspaces
• Exercise - Create a workspace
• Azure Machine Learning tools and interfaces
• Azure Machine Learning experiments
• Exercise - Run experiments
• Knowledge check
• Summary

• Introduction
• Run a training script
• Using script parameters
• Registering models
• Exercise - Training and registering a model
• Knowledge check
• Summary

• Introduction
• Introduction to datastores
• Use datastores
• Introduction to datasets
• Use datasets
• Exercise - Work with data
• Knowledge check
• Summary

• Introduction
• Introduction to environments
• Introduction to compute targets
• Create compute targets
• Use compute targets
• Exercise - Work with Compute Contexts
• Knowledge check
• Summary

• Introduction
• Introduction to pipelines
• Pass data between pipeline steps
• Reuse pipeline steps
• Publish pipelines
• Use pipeline parameters
• Schedule pipelines
• Exercise - Create a pipeline
• Knowledge check
• Summary

• Introduction
• Deploy a model as a real-time service
• Consume a real-time inferencing service
• Troubleshoot service deployment
• Exercise - Deploy a model as a real-time service
• Knowledge check
• Summary

• Introduction
• Creating a batch inference pipeline
• Publishing a batch inference pipeline
• Exercise - Create a batch inference pipeline
• Knowledge check
• Summary

• Introduction
• Defining a search space
• Configuring sampling
• Configuring early termination
• Running a hyperparameter tuning experiment
• Exercise - Tune hyperparameters
• Knowledge check
• Summary

• Introduction
• Automated machine learning tasks and algorithms
• Preprocessing and featurization
• Running automated machine learning experiments
• Exercise - Using automated machine learning
• Knowledge check
• Summary

• Introduction
• Understand differential privacy
• Configure data privacy parameters
• Exercise - Use differential privacy
• Knowledge check
• Summary

• Introduction
• Feature importance
• Using explainers
• Creating explanations
• Visualizing explanations
• Exercise - Interpret models
• Knowledge check
• Summary

• Introduction
• Consider model fairness
• Analyze model fairness with Fairlearn
• Mitigate unfairness with Fairlearn
• Exercise - Use Fairlearn with Azure Machine Learning
• Knowledge check
• Summary

• Introduction
• Enable Application Insights
• Capture and view telemetry
• Exercise - Monitor a model
• Knowledge check
• Summary

• Introduction
• Creating a data drift monitor
• Scheduling alerts
• Exercise - Monitor data drift
• Knowledge check
• Summary

• Introduction
• What is role-based access control?
• Exercise – Setup Azure Machine Learning with custom roles
• Keys and secrets with Azure Key Vault
• Secure your Azure Machine Learning network
• Exercise - Setup a secure Azure Virtual Network
• Knowledge check
• Summary