Luis Valencia explains what environments are in Azure ML:
An Environment defines Python packages, environment variables, and Docker settings that are used in machine learning experiments, including in data preparation, training, and deployment to a web service. An Environment is managed and versioned in an Azure Machine Learning Workspace. You can update an existing environment and retrieve a version to reuse. Environments are exclusive to the workspace they are created in and can’t be used across different workspaces.
In basic terms for a developer, it’s basically a Docker Image with all the needed dependencies (conda/pip packages) to run your experiment.
A friendly word of advice from some bad experiences: stick with the curated environments as much as you can. Those are easy and rarely fail. Building your own environments from Conda files is a possibility, but it’s an, err, probabilistic exercise as to whether your compute target will actually work or not.