How to Use Microsoft’s GitHub Copilot for Azure: A Step-by-Step Tutorial for AI-Driven Software Development

Microsoft’s GitHub Copilot for Azure is transforming how developers work by integrating AI directly into coding environments. This tutorial will walk you through setting up GitHub Copilot for Azure and demonstrate how to harness its AI capabilities for rapid coding, deployment, and cloud resource management. With AI suggestions, templates, and seamless Azure integration, Copilot enables developers to focus more on creativity and complex problem-solving.

Prerequisites

Before we dive in, here’s what you’ll need to get started:

• A GitHub account
• Access to GitHub Copilot
• An Azure account
• Visual Studio Code installed

For those new to Visual Studio Code, download it here and explore GitHub’s setup guide if needed.

Step 1: Setting Up GitHub Copilot in Visual Studio Code

GitHub Copilot is an AI-powered code completion tool that integrates with Visual Studio Code to assist in code writing and debugging.

1. Install the GitHub Copilot Extension:
   • Open Visual Studio Code.
   • Go to Extensions on the sidebar.
   • Search for “GitHub Copilot” and click Install.
   • Open the extension and follow the prompts to sign in with your GitHub account.
2. Enable Copilot for Azure Services:
   • In Visual Studio Code, open Settings.
   • Navigate to Extensions > GitHub Copilot and ensure Copilot for Azure is toggled on.

With this setup, Copilot will now start providing intelligent code suggestions directly within Visual Studio Code.

Step 2: Configuring Access Tokens for Azure in GitHub

To allow GitHub Copilot to integrate with your Azure environment, you’ll need to create an access token.

1. Generate an Azure Access Token:
   • Log into your Azure Portal.
   • Go to Azure Active Directory and select App registrations.
   • Click New registration and follow the instructions to create a token for Copilot.
   • Copy the generated token.
2. Link Access Token in GitHub Copilot:
   • In Visual Studio Code, open your Copilot settings and locate the Azure Integration section.
   • Paste the copied Azure access token to enable Copilot to interact with your Azure environment.

Step 3: Using GitHub Copilot for Real-Time Coding Assistance

Now that Copilot is set up with Azure, let’s dive into coding with real-time AI assistance.

1. Starting a New Project:
   • Open Visual Studio Code and create a new file (e.g., app.js for JavaScript).
   • Begin typing a comment such as // Create an Azure Storage container. Copilot will start suggesting relevant code snippets based on your prompt.
2. Example: Setting Up an Azure Storage Container:javascriptCopy code// Create a new Azure Storage Blob container const { BlobServiceClient } = require("@azure/storage-blob"); async function createContainer(containerName) { const connectionString = "<your-connection-string>"; const blobServiceClient = BlobServiceClient.fromConnectionString(connectionString); const containerClient = blobServiceClient.getContainerClient(containerName); await containerClient.create(); console.log(`Container "${containerName}" is created`); } createContainer("my-container"); As you type, Copilot offers code completions to help you set up and configure Azure resources like storage containers, streamlining code creation.

Step 4: Using AI App Templates in Azure for Rapid Prototyping

GitHub Copilot for Azure includes AI App Templates, which allow you to create applications quickly. Here’s how to use them:

1. Select a Template:
   • Open the Azure Portal and go to AI App Templates under Developer Tools.
   • Choose a template based on your project needs, such as a chatbot or an analytics dashboard.
2. Deploy the Template:
   • Click Deploy and specify configurations, including naming the app and selecting your subscription.
   • Copilot will guide you through the process, offering code suggestions as you customize the template within Visual Studio Code.
3. Modify the Template Using GitHub Copilot:
   • Once the template is deployed, open it in Visual Studio Code.
   • Use Copilot to modify features and add functionalities by typing comments or functions, like // Add sentiment analysis capability.
4. Example: Adding an AI Model for Sentiment Analysis:pythonCopy code# Python example for integrating a sentiment analysis model from azure.ai.textanalytics import TextAnalyticsClient from azure.core.credentials import AzureKeyCredential # Setup client def authenticate_client(): ta_credential = AzureKeyCredential("<your-key>") text_analytics_client = TextAnalyticsClient( endpoint="<your-endpoint>", credential=ta_credential ) return text_analytics_client # Sentiment analysis function def sentiment_analysis(client, document): response = client.analyze_sentiment(documents=[document])[0] print(f"Document sentiment: {response.sentiment}") print("Overall scores:", response.confidence_scores) client = authenticate_client() sentiment_analysis(client, "I love using Microsoft Azure!") This code sets up a basic sentiment analysis model using Azure’s Text Analytics API. GitHub Copilot can assist in writing the setup and analysis functions based on simple comments, making it easier to integrate AI capabilities into your applications.

Step 5: Deploying and Managing Your Application in Azure

Once your application is set up, use Copilot to deploy and manage it within Azure Kubernetes Service (AKS) or Azure App Services.

1. Deploying an Application with AKS:
   • Open your project in Visual Studio Code.
   • In the terminal, use Copilot to generate Kubernetes configuration files by typing comments like # Create Kubernetes deployment.
   Example YAML file generated by Copilot:yamlCopy codeapiVersion: apps/v1 kind: Deployment metadata: name: my-app spec: replicas: 3 selector: matchLabels: app: my-app template: metadata: labels: app: my-app spec: containers: - name: my-app-container image: <your-container-image> ports: - containerPort: 80
2. Launch the App:
   • Use the kubectl apply -f deployment.yaml command to deploy.
   • Access your application in the Azure Portal to manage scaling, monitoring, and updating configurations as needed.

Conclusion: Taking Advantage of AI-Driven Software Development with GitHub Copilot for Azure

GitHub Copilot for Azure simplifies software development by providing real-time AI guidance and integration with Azure’s cloud resources. From code generation to deployment, Copilot offers a streamlined way to build, test, and deploy applications, allowing developers to focus on innovation and complex problem-solving.

By following this tutorial, you’ll be well on your way to building, deploying, and scaling AI-driven applications in Azure, fully supported by GitHub Copilot’s powerful AI features. To explore more Copilot capabilities, check out the official GitHub Copilot documentation for additional resources.