1.12 – Deleting Resources and Wrap Up Lesson

Lesson Overview

Resource management is an essential skill in any cloud-based workflow. In this lesson, you’ll learn how to safely delete resources created in your Azure Machine Learning workspace. This process is more than just good housekeeping—it ensures you aren’t billed for services and resources you no longer need. Projects often involve multiple endpoints, models, and supporting resources that are tied together. If left unattended, these can lead to extra costs or clutter in your Azure account.

This lesson wraps up your first advanced project in Azure ML by guiding you through the process of selecting, deleting, and confirming the removal of resources. If some resources can’t be deleted on the first try due to dependencies, you’ll see how to address these common roadblocks. By handling both child (dependent) and parent (container) resources, you’ll gain a practical understanding of Azure’s organizational logic and avoid billing mistakes.

This cleanup process is useful for anyone running projects, testing approaches, or sharing environments with others. You’ll use these skills any time you close a project—whether for a job, a class, or your own experiments.

Who This Is For

If you’ve finished building and deploying models in Azure and want to tidy up your project, this lesson is for you. This lesson will help:

• Data scientists closing out test or research environments
• Developers running proof-of-concept AI projects
• IT administrators looking to keep cloud costs under control
• Students or educators completing classroom assignments
• Business analysts working with shared Azure accounts
• Anyone interested in efficient resource management in Azure

Where This Fits in a Workflow

Resource cleanup is the final step after model development and deployment in Azure Machine Learning. Once your endpoint is published and you’ve explored different models, you’ll want to delete everything you don’t plan to use further. For example, a developer finishing a proof-of-concept app will clean up their workspace to avoid extra billing. Or a data science student will delete resources at the end of a class module.

This practice ensures your account stays organized, keeps costs predictable, and gets you ready for new projects without leftover clutter. Regular cleanup is part of the best practices in cloud work—just as important as setup and deployment.

Technical & Workflow Benefits

Before deleting resources, unused endpoints and artifacts would often remain in an account, sometimes leading to unwanted charges. The method taught in this lesson streamlines the cleanup process by selecting all resources with one action, automatically handling dependencies in the order Azure requires. When a resource can’t be deleted because its “child” components are still present, you’ll know how to troubleshoot instead of giving up or leaving things behind.

This approach saves time compared to deleting assets one-by-one, and prevents billing surprises at month’s end. For example, after running a quick test deployment, IT admins can now fully retire all the project’s resources in minutes, rather than tracking down every linked item manually. The end result: consistent, clean workspaces and clear, manageable costs.

Practice Exercise

Choose an existing Azure Machine Learning workspace where you’ve created several resources (such as models, deployments, or datasets). Follow these steps:

1. Identify the resource group that holds all your Azure Machine Learning assets from this project.
2. Select all resources for deletion, and if the interface requires, confirm by typing “delete.” If the process fails for some items, sort resources by type and manually remove child resources before deleting the parent workspace.
3. Once all other resources are gone, delete the resource group and verify it is empty.

Reflection: Did you notice any resources that couldn’t be deleted right away? How did handling dependencies compare to deleting single, unrelated items?

Course Context Recap

With this lesson, you’ve reached the end of building and deploying your first advanced AI model using Microsoft Azure. Up to this point, you’ve created, experimented, and launched machine learning solutions in the cloud. This final step helps you close your project safely and supports good project management habits. As you move forward, refer back to this lesson anytime you start or end new Azure ML projects—proper cleanup keeps your workflow secure and your costs low. Ready to continue learning? Explore the rest of the course for more advanced Azure skills and best practices.