Introducing the Azure Machine Learning Service
Machine Learning (ML), leveraging data to build and train a model to make predictions, is rapidly maturing. Azure Machine Learning (AML) is Microsoft’s cloud service, which not only enables model development but also your data science life cycle. AML is a tool designed to empower data scientists, ML engineers, and citizen data scientists. It provides a framework to train and deploy models empowered through MLOps to monitor, retrain, evaluate, and redeploy models in a collaborative environment backed by years of feedback from Microsoft’s Fortune 500 customers.
In this chapter, we will focus on deploying an AML workspace, the resource that leverages Azure resources to provide an environment to bring together the assets you will leverage when you use AML. We will showcase how to deploy these resources using a Guided User Interface (GUI), followed by setting up your AML service via the Azure Command-Line Interface (CLI) ml extension (v2), which is the ml extension for the Azure CLI, allowing model training and deployment through the command line. We will proceed with setting up the workspace by leveraging Azure Resource Management (ARM) templates, which are referred to as ARM deployments.
During deployment, key resources will be deployed, including AML Studio, a portal for data scientists to manage their workload, often referred to as your workspace; Azure Key Vault for storing sensitive information; Application Insights for logging information; Azure Container Registry to store docker images to leverage; and an Azure storage account to hold data. These resources will be leveraged behind the scenes as you navigate through the Azure Machine Learning service workspace, creating compute resources for writing code by leveraging the Integrated Development Environments (IDE) of your choice, including Jupyter Notebook, Jupyter Lab, as well as VS Code.
In this chapter, we will cover the following topics:
- Building your first AMLS workspace
- Navigating AMLS
- Creating a compute for writing code
- Developing within AMLS
- Connecting AMLS to VS Code
