Running ML experiments with Azure Machine Learning
So far, we have installed the Azure CLI locally, deployed our ML workspace to our Azure subscription, and had a look through the features and functionalities of the Azure Machine Learning workspace.
In this final section of the chapter, we will set up our local environment, including Python, the Azure Machine Learning Python SDK, and optionally Visual Studio Code, and embark on our first experiments locally and with compute targets in the cloud.
Setting up a local environment
In the beginning, we discussed briefly the tooling available for deploying Azure resources through Azure Resource Manager. In the same vein, let's have a look at the options for authoring and orchestrating the workspace from our local environment. The options are as follows:
- Using Python 3, the Azure Machine Learning Python SDK, a Jupyter Python extension, and the Azure ML CLI (1.0/2.0) extension (and an editor of choice)
- Using Python3...