In this lab, you’ll practice creating and managing your machine learning workspace on Azure. When you’re finished with this lab, you’ll have a working Azure Machine Learning Workspace where you can run machine learning experiments.
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Miguel Saavedra is an author, solutions architect, and Instructor specializing in AWS, big data, automation, and security. He has worked in several companies in the finance/fintech, education, and medical industries as well as some government projects. He has published and conducted research on both cloud and on-premise solutions for big data focusing on network analytics, and machine learning. He also designs highly available and automated CICD toolchains for high throughput microservices on AW... moreS.
An Azure Machine Learning Workspace is a centralized place to manage all of the Azure ML assets you need to work on a machine learning project. In this challenge, you will be creating this workspace using the Azure Portal.
Create a Compute Instance in Your Workspace
Now that you have a running workspace, you will need a place to run your programs and scripts. You will now explore the Azure Machine Learning studio and create your first compute instance in your workspace.
Upload and Run a Python Notebook
Now that you have a working compute instance, you can now run scripts and experiments. In this challenge, you will be running Python scripts in a Jupyter Notebook to display information about your workspace.
Provided environment for hands-on practice
We will provide the credentials and environment necessary for you to practice right within your browser.
Follow along with the author’s guided walkthrough and build something new in your provided environment!
Did you know?
On average, you retain 75% more of your learning if you get time for practice.