Build and operate machine learning solutions with Azure Machine Learning 

Azure Machine Learning workspaces

Exercise - Create a workspace

Azure Machine Learning tools and interfaces

Azure Machine Learning experiments

Run a training script

Using script parameters

Registering models

Exercise - Training and registering a model

Exercise - Run experiments

Introduction to datastores

Use datastores

Introduction to datasets

Use datasets

Exercise - Work with data

Introduction to environments

Introduction to compute targets

Create compute targets

Use compute targets

Exercise - Work with Compute Contexts

Introduction to pipelines

Pass data between pipeline steps

Reuse pipeline steps

Publish pipelines

Use pipeline parameters

Schedule pipelines

Exercise - Create a pipeline

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

Creating a batch inference pipeline

Publishing a batch inference pipeline

Exercise - Create a batch inference pipeline

Defining a search space

Configuring sampling

Configuring early termination

Running a hyperparameter tuning experiment

Exercise - Tune hyperparameters

Automated machine learning tasks and algorithms

Preprocessing and featurization

Running automated machine learning experiments

Exercise - Using automated machine learning

Understand differential privacy

Configure data privacy parameters

Exercise - Use differential privacy

Feature importance

Using explainers

Creating explanations

Visualizing explanations

Exercise - Interpret models

Consider model fairness

Analyze model fairness with Fairlearn

Mitigate unfairness with Fairlearn

Exercise - Use Fairlearn with Azure Machine Learning