Azure Machine Learning

In this article, we’ll learn about Azure Machine Learning and create a Machine Learning Workspace which we’ll use for our project on the Azure Machine Learning Series. This is the first article in the Azure Machine Learning Series.

Microsoft AI

Microsoft AI is a powerful framework that enables organizations, researchers, and non-profits to use AI technologies with its powerful framework which offers services and features across domains of Machine Learning, Robotics, Data Science, IoT, and many more. Learn more about Microsoft AI from this article.

The Azure Machine Learning enriches and consolidates the functionalities to support model training and deployment which transitions from Machine Learning Studio. It provides tools for Machine Learning works for all skill levels, provides an open and interoperable framework with support to different languages, and enables robust end-to-end MLOps. It also supports Automated Machine Learning. Read this article Auto ML to learn more about it.

So, where and how do we start if we want to create and deploy a Machine Learning project? Azure Machine Learning provides all the tools through its portal to create the resources and set up the infrastructure that is needed for any kind of machine learning works.

Let us start with first creating Workspace for Machine Learning in Azure.

Step 1

First of all, Sign in to Azure Portal. You’ll be taken to the welcome page that looks similar to the one as follows.

Step 2

Click on Create a Resource.

Step 3

Under Categories, Select AI + Machine Learning.

We can then see the Machine Learning option on products, Click that.

Step 4

Now, we are provided with the details to be filled in for creating the ML Workspace.

FIrst and Foremost, we need to either have a paid Azure Subscription or a Sponsorship Pass to create this workspace. Here I’ve used the Azure Pass.

Step 5

Now, Create the resource group for the workspace.

Step 6

Fill in the workspace name and select the Region. The Storage Account, Key Vault and Application Insights will be automatically filled.

Step 7

Create a new Container Registry.

Step 8

Now, as all details are filled, Click on Review + Create.

Step 9

Validation is done and as it is passed, we can see the Confirmation.

Step 10

Next, Click on Create.

Step 11

We’ll be notified with the Deployment Progress.

As the deployment is succeeded, we can see it in the notification bar.

Step 12

As the Deployment is completed, we’ll be provided with the option to Check the resource we created.

Click on the Go to Resource.

Step 13

Here, we can see, the workspace has been created with the Studio Web URL, Registry, Key Vault, Application Insights and many other details.

Here, Click on Launch Studio.

We’ll now be taken to the Microsoft Azure Machine Learning Studio. This enables us to perform any Machine Learning Tasks from running notebooks, training and testing models, deployment of models and many more functionalities offered by Azure Machine Learning Studio.

Delete Workspace

Step 14

Later, once we are done with our project, training and testing models and deploying it and if we don’t require it, it is always supported to Delete the resource.

We can do this by visiting the Azure Portal and selecting the Worksapce Resource.

On the Menu Bar, we can see the Delete option.

We’ll then be notified with the Workspace deletion.

Conclusion

Thus, in this article, we learned about Azure Machine Learning and went through a step-by-step tutorial to create a Machine Learning Workspace in Azure. This workspace will allow us to use the Azure ML Studio and thus provide end-to-end support for the entire Machine Learning Workflow.