1.3 – Setting Up Machine Learning Resources Lesson

Lesson Overview

Before you can train models or run experiments in Azure, it’s important to have the right workspace and supporting resources in place. This lesson covers the essentials of provisioning an Azure Machine Learning workspace using the Azure portal, making sure your environment is ready for practical AI development. You’ll see how to create a resource group—a logical collection where related assets are managed—ensuring your work stays organized as your projects expand.

Choosing a data center region and understanding resource naming play a big role in fully utilizing Azure’s tools. The lesson also explains supplemental resources, such as storage accounts and key vaults, which support your machine learning operations behind the scenes. While some configuration options and resources are more relevant in production, you’ll focus here on what’s practical for a hands-on learning experience.

This foundation is crucial for anyone building, deploying, or managing AI workflows in Azure. Whether you work in business analytics, data science, education, or technology, knowing how to set up your resources properly eliminates confusion and helps keep future projects secure and scalable. The skill to set up and understand your workspace is often a requirement in professional environments, where multiple solutions and roles rely on a well-organized cloud setup.

Who This Is For

This lesson is a match for anyone starting out with Azure Machine Learning or aiming to solidify their foundational cloud skills:

• Data scientists building, training, or deploying models in Microsoft Azure
• Analysts and researchers organizing datasets and experiments in the cloud
• Educators or students setting up class or demo environments
• Developers integrating Azure ML capabilities into larger projects
• Business professionals evaluating Azure for AI projects
• Anyone interested in learning about managing cloud AI resources efficiently

Where This Fits in a Workflow

Setting up your workspace and resources is a critical early step in any AI project workflow. You’ll usually complete this process before doing any data upload, experiment configuration, or model training. For example, when planning a new AI solution—such as automated document processing—you'd start with resource provisioning in Azure, ensuring all tools and environments are prepared for development and testing.

Another use case is preparing a sandbox environment for training or demos, giving yourself and others a risk-free space to experiment with different AI features. This step provides the base for reliable, organized, and scalable projects, so you avoid backtracking or running into unexpected roadblocks later.

Technical & Workflow Benefits

Provisioning resources using Azure’s guided approach significantly improves how you manage and scale machine learning work. Compared to manually setting up each component—such as storage, security, and region placement—Azure Machine Learning’s workspace provisioning automatically links and configures these elements with default settings that are suitable for most scenarios.

In a traditional workflow, you might have to set up storage, permissions, and monitoring one at a time. Here, supporting resources like storage accounts, key vaults, and application monitoring are created for you once you initiate a workspace. This not only reduces setup time, but also leads to more consistent configuration and fewer errors, especially important in collaborative or educational settings. The result is a durable environment ready to support experiments, data, and models without constant manual adjustments.

Practice Exercise

To reinforce what you’ve seen, try setting up a practice machine learning workspace in your Azure account:

1. Create a new resource group and name it according to your current project or learning objective.
2. Provision a new Azure Machine Learning workspace within that group, selecting your preferred (or the suggested) region.
3. After deployment, review the set of automatically generated resources (storage, key vault, etc.) in your resource group and note their relationship to your workspace.

Reflection: How does using a logical resource group help keep your Azure assets organized as projects expand or as you work on multiple solutions across different regions?

Course Context Recap

This lesson is an early but essential part of your journey in building AI models with Microsoft Azure. After learning the basics of your account setup and portal navigation, you now have the skills to provision the key resources that form the backbone of any Azure-based machine learning project. Up next, you’ll move from foundational setup into data preparation and workspace exploration—continuing to build practical skills for real-world AI development. Stay in the course to explore how each piece works together to create a powerful cloud-based AI workflow.