Determine the appropriate compute specifications for a training workload
Describe model deployment requirements
Select which development approach to use to build or train a model
Create an Azure Machine Learning workspace
Manage a workspace by using developer tools for workspace interaction
Set up Git integration for source control
Select Azure Storage resources
Register and maintain datastores
Create and manage data assets
Create compute targets for experiments and training
Select an environment for a machine learning use case
Configure attached compute resources, including Apache Spark pools
Monitor compute utilization
Access and wrangle data during interactive development
Wrangle interactive data with Apache Spark
Create a training pipeline