Empowering Intelligence with Azure Machine Learning

Introduction

In today's world, we live in the era of Generative AI and Large Language Models; many organizations endeavor to extract valuable insights and drive creativity through machine learning. To build an intelligent application and generate data-driven predictions and recommendations, you can use the Azure Machine Learning service. Whether you are a data scientist, data engineer, or data analyst, Azure Machine Learning facilitates the machine learning workflows and accelerates your destination towards building intelligent business solutions. In this article, I will discuss the detailed steps to create and deploy the Azure Machine Learning service comprehensively.

Azure Machine Learning 

Azure Machine Learning is a cutting-edge technology provided by Microsoft Azure. Azure Machine Learning is a fully managed service used to train, deploy, and manage machine learning models to a larger extent.

Steps to create Azure Machine Learning Studio

Sign in to the Azure portal at https://portal.azure.com/

In the search bar, type Azure Machine Learning.

In the Azure Machine Learning window, click Create button.

In the Create tab, click the New Workspace option.

In the Basics tab, choose the subscription first and type the Resource group name as testRG.

In the workspace details, type the following workspace name as retailws and region as East US.

Storage account, Key vault, and Application insights values will be taken default.

Choose container registry as none.

You will get the Validation passed message, which is appeared on the screen.

Click Create button.

Deployment started initialized in a minute or two this became successful.

Click the Go to resource button.

Click the Launch Studio button.

Once the button is clicked, Microsoft Azure Learning Studio will be displayed on the screen.

In the Manage tab, click Compute button.

In the Compute tab, Click Compute instances.

Click the New option in the Compute menu.

In the Create compute instance tab, type compute name as retailcs.

Choose location as eastus and choose virtual machine type as CPU.

Select from recommended options in the Virtual machine size.

Click Create button.

The retailcs compute instance started creating, and it will take a minute or two to be deployed.

Now the state Creating becomes Running in the Compute instances tab.

In the Notebooks section, you can create a notebook file name as follows sample.ipynb

The users can type the Python statements as follows, and also you can see the results in the cell.

Summary

Azure Machine Learning empowers organizations to make data driven predictions, recommendations, and automate business workflows, gain valuable insights. In this article, we successfully created and deployed the Azure Machine Learning service, created a Python Notebook, and executed Python statements in the Azure Machine Learning Studio.

I hope you have enjoyed reading this article!!! And for more updates, stay tuned on C# Corner.

Happy Learning!!!