Select Azure Storage resources
Identify an appropriate framework to package a model
Configure compute for a job run
Use automated machine learning for tabular data
Set up Git integration for source control
Describe MLflow model output
Configure job run settings for a script
Evaluate the model, including responsible AI guidelines
Manage a workspace by using developer tools for workspace interaction
Use component-based pipelines
Define early termination options
Use custom code components in designer
Create an Azure Machine Learning workspace
Define the primary metric
Consume data assets from the designer