1.4 – Azure Machine Learning Overview Lesson

Lesson Overview

The Azure Machine Learning Workspace brings together all of the tools you need to build, train, and manage machine learning models in the cloud. This lesson gives you a tour of the workspace’s main interface so you can get comfortable navigating its menus and resources before starting your own work. You’ll learn what each section does—from uploading data assets, managing compute instances, to browsing available AI models. The lesson highlights practical features like Prompt Flow for generative AI, accessing pre-built notebook examples, and exploring the ever-changing model catalog. You’ll also see how to connect external resources like storage or OpenAI services to your workspace, which is especially helpful when dealing with large datasets or specific AI tools.

Understanding the layout and options within the Azure Machine Learning Workspace streamlines your workflow and helps you avoid confusion as projects grow in complexity. Whether you’re fine-tuning a prebuilt model, working through sample code, or setting up a custom training job, knowing where to find each tool saves time and reduces errors. For anyone aiming to use Azure for advanced AI applications, this overview is a practical first step.

Who This Is For

If you’re just starting with cloud machine learning or moving to Azure for more advanced projects, this lesson addresses the needs of multiple roles and backgrounds:

• Developers new to Azure Machine Learning tools
• Data scientists exploring automated and custom model workflows
• Educators designing assignments on cloud-based AI projects
• Analysts evaluating or testing machine learning solutions on Azure
• Project leads setting up data and compute configurations for AI teams
• Anyone interested in experimenting with generative AI and Prompt Flow on Azure

Where This Fits in a Workflow

Getting familiar with the Azure Machine Learning Workspace is one of the first steps when working on a cloud-based AI project. Before uploading data or training a model, you need to know where to locate key functions like uploading datasets, setting up compute resources, and finding model templates. For instance, if you want to quickly test several different AI models on your data, you’ll start by exploring the model catalog and AutoML tools introduced here. Or, if you plan to write custom code, the lesson shows you where to find notebooks and sample projects.

This navigation knowledge is also crucial for troubleshooting—if something fails (like a job or compute resource), you’ll know where to check for status and logs. Mastering the workspace menu and options at the outset removes friction once you move into model training, experiment tracking, or resource scaling later in your workflow.

Technical & Workflow Benefits

Before Azure Machine Learning, running machine learning projects in the cloud often meant managing separate tools for storage, compute, model management, and automation. With the workspace, all essential resources appear under one unified platform—models, compute, datasets, and external connections are just a few clicks away. This setup reduces the need for manual tracking and configuration, so you spend less time switching between services and more on productive AI development.

For example, developers can save setup time by using pre-built code notebooks and dataset templates, while project managers can easily monitor job results and resource usage from a single dashboard. Connecting external compute (like Kubernetes clusters or Databricks) and integrating with other Azure services is also much easier thanks to the workspace’s built-in connections panel. Whether your goal is faster prototyping with AutoML or streamlined management for large teams, using Azure Machine Learning Workspace increases both speed and consistency across the project lifecycle.

Practice Exercise

To get comfortable with the Azure Machine Learning Workspace, try these steps in your own Azure Portal:

1. Open your Azure Machine Learning Workspace and explore the main menu. Identify and list the key areas such as the model catalog, notebooks, compute, and connections.
2. Find the area where you would upload data assets. Take a screenshot or write down the steps to start adding your own dataset.
3. Click on compute resources and note the different types of compute (instance, cluster, external). Which type do you think would fit best for your first small experiment?

Reflect: After exploring, do you feel confident navigating the workspace? What area seems most relevant to your project, and which features would you want to investigate further?

Course Context Recap

This lesson builds your foundation in using Azure Machine Learning by showing you the workspace’s main menu, resources, and features. Previously, you prepared your Azure environment and set up the initial workspace. Next, you’ll move forward by gathering and uploading data for your AI project—an essential step for model training. Continue with the next lesson to learn how to prepare and manage data in Azure Machine Learning, or explore the full course to broaden your skills in building and deploying advanced AI solutions on Microsoft Azure.