Get Started with Azure Machine Learning

This tutorial will guide you through the essential steps to set up and begin your first machine learning project using Azure Machine Learning. You'll learn how to create an Azure ML workspace, connect to it, and perform basic operations.

Prerequisites

An Azure subscription. If you don't have one, you can create a free account.
Azure CLI installed and configured. Install Azure CLI.
Python 3.7 or later installed. Download Python.

Step 1: Create an Azure Machine Learning Workspace

An Azure Machine Learning workspace is the top-level resource for Azure Machine Learning. It provides a centralized place to work with all the artifacts you create when you use Azure Machine Learning.

You can create a workspace using the Azure portal, Azure CLI, or SDKs. We'll use the Azure CLI for this tutorial.

Note: Make sure you are logged into your Azure account via the Azure CLI: az login.

Run the following commands in your terminal:

                    
az group create --name my-ml-resource-group --location eastus
az ml workspace create --name my-ml-workspace --resource-group my-ml-resource-group --location eastus

These commands will create a new resource group and then create your Azure Machine Learning workspace within that group.

Step 2: Install the Azure ML SDK for Python

The Azure ML SDK for Python allows you to interact with your workspace programmatically.

Install the SDK using pip:


pip install azure-ai-ml azure-identity

Step 3: Connect to Your Workspace

Now, let's connect to your newly created workspace using Python.

                    
from azure.ai.ml import MLClient
from azure.identity import DefaultAzureCredential

# Define your workspace details
subscription_id = ""
resource_group = "my-ml-resource-group"
workspace_name = "my-ml-workspace"

# Authenticate and create the MLClient object
credential = DefaultAzureCredential()
ml_client = MLClient(credential, subscription_id, resource_group, workspace_name)

print(f"Connected to workspace: {ml_client.workspace_name}")
                    
                

Remember to replace <YOUR_SUBSCRIPTION_ID> with your actual Azure subscription ID.

Step 4: Explore Workspace Information

You can retrieve various details about your workspace:

                    
print(f"Workspace ID: {ml_client.workspace_id}")
print(f"Location: {ml_client.location}")
print(f"Creation time: {ml_client.creation_time}")

Congratulations! You have successfully created an Azure Machine Learning workspace and connected to it. You are now ready to start building and deploying machine learning models.