Microsoft's Student Opportunities: A Gateway to Professional Growth
Are you a student looking to give your career in tech a boost? Look no further than Microsoft's student opportunities. From scholarships to internships, Microsoft provides a range of programs designed to help students develop their skills, gain practical experience, and build connections in the industry. In this article, we'll explore Microsoft's opportunities and events, and how they can be the gateway to professional growth for students seeking a career in technology.Make your own private ChatGPT
Introduction Creating your own private ChatGPT allows you to leverage AI capabilities while ensuring data privacy and security. This guide walks you through building a secure, customized chatbot using tools like Azure OpenAI, Cosmos DB and Azure App service. Why Build a Private ChatGPT? With the rise of AI-driven applications, organizations, people often face challenges related to data privacy, customization, and integration. Building a private ChatGPT addresses these concerns by: Maintaining Data Privacy: Keep sensitive information within your infrastructure. Customizing Responses: Tailor the chatbot’s behavior and language to suit your requirements. Ensuring Security: Leverage enterprise-grade security protocols. Avoiding Data Sharing: Prevent your data from being used to train external models. If organizations do not take these measures their data may go into future model training and can leak your sensitive data to public. Eg: Chatgpt collects personal data mentioned in their privacy policy Prerequisites Before you begin, ensure you have: Access to Azure OpenAI Service. A development environment set up with Python. Basic knowledge of FastAPI and MongoDB. An Azure account with necessary permissions. If you do not have Azure subscription, try Azure for students for FREE. Step 1: Set Up Azure OpenAI Log in to the Azure Portal and create an Azure OpenAI resource. Deploy a model, such as GPT-4o (multimodal), and note down the endpoint and API key. Note there is also an option of keyless authentication. Configure permissions to control access. Step 2: Use Chatgpt like app sample You can select any repository to be as base template for your app, in this I will be using the third option AOAIchat. It is developed by me. GitHub - mckaywrigley/chatbot-ui: AI chat for any model. Azure-Samples/azure-search-openai-demo: A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. sourabhkv/AOAIchat: Azure OpenAI chat This architecture diagram represents a typical flow for a private ChatGPT application with the following components: App UX (User Interface): This is the front-end application (mobile, web, or desktop) where users interact with the chatbot. It sends the user's input (prompt) and displays the AI's responses. App Service: Acts as the backend application, handling user requests and coordinating with other services. Functions: Receives user inputs and prepares them for processing by the Azure OpenAI service. Streams AI responses back to the App UX. Reads from and writes to Cosmos DB to manage chat history. Azure OpenAI Service: This is the core AI service, processing the user input and generating responses using models like GPT-4o. The App Service sends the user input (along with context) to this service and receives the AI-generated responses. Cosmos DB: A NoSQL database used to store and manage chat history. Operations: Writes user messages and AI-generated responses for future reference or analysis. Reads chat history to provide context for AI responses, enabling more intelligent and contextual conversations. Data Flow: User inputs are sent from the App UX to the App Service. The App Service forwards the input (with additional context, if needed) to Azure OpenAI. Azure OpenAI generates a response, which is streamed back to the App UX via the App Service. The App Service writes user inputs and AI responses to Cosmos DB for persistence. This architecture ensures scalability, secure data handling, and the ability to provide contextual responses by integrating database and AI services. What can you do with my template? AOAIchat supports personal, enterprise chat enabled by RAG People can enable RAG mode if they want to search within their database, else it behaves like normal ChatGPT. It supports multimodality, (supports image, text input) also depends on model deployed in Azure AI foundry. Step 3: Deploy to Azure Deploy a Cosmos DB account in nearest region Deploy Azure OpenAI model (gpt-4o, gpt-4o-mini recommended) Deploy Azure App service, try using container I would recommend B1plan to your nearest region, select docker registry sourabhkv/aoaichatdb:0.1 startup command uvicorn app:app --host 0.0.0.0 --port 80 After app service starts, put all environment variables The application requires the following environment variables to be set for proper configuration: Environment Variable Description AZURE_OPENAI_ENDPOINT The endpoint for Azure OpenAI API. AZURE_OPENAI_API_KEY API key for accessing Azure OpenAI. DEPLOYMENT_NAME Azure OpenAI deployment name. API_VERSION API version for Azure OpenAI. MAX_TOKENS Maximum tokens for API responses. MONGO_DETAILS MongoDB connection string. AZURE_OPENAI_ENDPOINT=<your_azure_openai_endpoint> AZURE_OPENAI_API_KEY=<your_azure_openai_api_key> DEPLOYMENT_NAME=<your_deployment_name> API_VERSION=<your_api_version> MAX_TOKENS=<max_tokens> MONGO_DETAILS=<your_mongo_connection_string> Optional feature: implement authentication to secure access. Within app service select Authentication and select service providers. I went with Entra based authentication with single tenant. There is option of multi-tenant, personal accounts as well. Restart App service and within 2 minutes your private ChatGPT is ready. Pricing Pricing may depend on the plan you have deployed resources and region. Check Azure calculator for price estimation. My estimate for pricing I deployed all my resources in Sweden central Cosmos DB config - Cosmos DB for MongoDB (RU) serverless config with single write master, 2 GB transactional storage, 2 backup plan (FREE) ~ 0.75$ Azure OpenAI service - plan S0, model gpt-4o-mini global deployment, Input 20000 tokens, Output 10000 tokens ~ 9.00$ App service plan - OS Linux, Tier B1, instance count 1 ~13.14$ Total monthly cost = 22.89$ This price may vary in future, in region I calculated my configuration in Azure calculator Governance Azure OpenAI provides content filters to block any kind of input that violates responsible AI practices. Categories include Hate and Fairness Sexual Violence Self-harm User Prompt Attacks (direct and indirect) The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Azure OpenAI Service includes default safety settings applied to all models set as medium. Content filters can be modified to different level depending on use case. It supports RAG, I have provided detailed solution for it in my GitHub. Practical implementation GE Aerospace, in partnership with Microsoft and Accenture, has launched a company-wide generative AI platform, leveraging Microsoft Azure and Azure OpenAI Service. This solution aims to transform asset tracking and compliance in aviation, enabling quick access to maintenance records and reducing manual processing time from days to minutes. It supports informed decision-making by providing insights into aircraft leasing, compliance gaps, and asset health. For enterprises implementing private ChatGPT solutions, this illustrates the potential of generative AI for streamlining document-intensive processes while ensuring data security and compliance through cloud-based infrastructure like Azure. GE Aerospace Launches Company-wide Generative AI Platform for Employees | GE Aerospace News Build your own private ChatGPT style app with enterprise-ready architecture - By Microsoft Mechanics How to make private ChatGPT for FREE? It can be FREE if all of the setup is running locally on your hardware. Cosmos DB <-> MongoDB. Azure OpenAI <-> Ollama / LM studio Refer this NOTE : I have used gpt-4o, gpt-4o-mini these values are hardcoded in webpage, if you are using other models, you might have to change them in index.html. App Service <-> Local machine Register for Github models to access API for FREE. Note: GitHub models have rate limit for different models. Useful links sourabhkv/AOAIchat: Azure OpenAI chat What is RAG? Get started with Azure OpenAI API Chat with Azure OpenAI models using your own dataUtilizando un archivo en GitHub Copilot para Visual Studio
Cuando creas un nuevo proyecto desde cero en Visual Studio, algunos archivos se crean. Hay muchas plantillas disponibles, para muchos tipos de aplicaciones, desde aplicaciones simples hasta aplicaciones web complejas, así como aplicaciones móviles, sin servidor y muchas más. Todos estos proyectos constan de varios archivos. Tienes tus archivos de código, que contienen el software que se ejecutará, organizados en clases, frecuentemente cada clase en su propio archivo. Tienes los archivos de configuración, ya sea JSON, XML, YAML u otros. Incluso puedes tener archivos de datos, incrustados en la aplicación cuando está construida. En un video que se publicó, mi compañera Gwyn muestra cómo puedes usar el atajo Hash (#) para hacer referencia a otro archivo. [Este post es una traducción del blog original escrito en inglés por Laurent Bugnion y Gwyn Peña-Sigüenza] El contexto lo es todo Como mencionamos en varias ocasiones, lo que hace que una respuesta de GitHub Copilot sea buena comienza con un buen prompt. Sin embargo, el prompt no es solo pedirle al modelo de lenguaje que haga algo; también es necesario proporcionar contexto. En el mundo de la IA, nos referimos a esto como 'grounding' del modelo con datos, o Generación Aumentada por Recuperación (RAG). A través de su entrenamiento, Copilot tiene acceso a conocimientos generales sobre la plataforma que estás utilizando, así como a conocimientos específicos sobre bibliotecas y frameworks. Sin embargo, lo que falta es tu propio código privado, el código que el resto del mundo no ve. Por ejemplo, puedes informar a GitHub Copilot que otro archivo contiene una serie de métodos que la clase actual puede utilizar. En el ejemplo, Gwyn le indica a GitHub Copilot un archivo JSON que contiene datos para generar una prueba. Esto añade un contexto valioso, permitiendo que Copilot genere el código correcto de manera más rápida. Más información Como siempre, puedes encontrar muchos recursos educativos gratuitos en esta colección de Microsoft Learn. Y, por supuesto, la mejor manera de estar al día es suscribirse al canal de YouTube de Visual Studio, al Visual Studio DevBlog y, por supuesto, a este blog.Learn How to Build Smarter AI Agents with Microsoft’s MCP Resources Hub
If you've been curious about how to build your own AI agents that can talk to APIs, connect with tools like databases, or even follow documentation you're in the right place. Microsoft has created something called MCP, which stands for Model‑Context‑Protocol. And to help you learn it step by step, they’ve made an amazing MCP Resources Hub on GitHub. In this blog, I’ll Walk you through what MCP is, why it matters, and how to use this hub to get started, even if you're new to AI development. What is MCP (Model‑Context‑Protocol)? Think of MCP like a communication bridge between your AI model and the outside world. Normally, when we chat with AI (like ChatGPT), it only knows what’s in its training data. But with MCP, you can give your AI real-time context from: APIs Documents Databases Websites This makes your AI agent smarter and more useful just like a real developer who looks up things online, checks documentation, and queries databases. What’s Inside the MCP Resources Hub? The MCP Resources Hub is a collection of everything you need to learn MCP: Videos Blogs Code examples Here are some beginner-friendly videos that explain MCP: Title What You'll Learn VS Code Agent Mode Just Changed Everything See how VS Code and MCP build an app with AI connecting to a database and following docs. The Future of AI in VS Code Learn how MCP makes GitHub Copilot smarter with real-time tools. Build MCP Servers using Azure Functions Host your own MCP servers using Azure in C#, .NET, or TypeScript. Use APIs as Tools with MCP See how to use APIs as tools inside your AI agent. Blazor Chat App with MCP + Aspire Create a chat app powered by MCP in .NET Aspire Tip: Start with the VS Code videos if you’re just beginning. Blogs Deep Dives and How-To Guides Microsoft has also written blogs that explain MCP concepts in detail. Some of the best ones include: Build AI agent tools using remote MCP with Azure Functions: Learn how to deploy MCP servers remotely using Azure. Create an MCP Server with Azure AI Agent Service : Enables Developers to create an agent with Azure AI Agent Service and uses the model context protocol (MCP) for consumption of the agents in compatible clients (VS Code, Cursor, Claude Desktop). Vibe coding with GitHub Copilot: Agent mode and MCP support: MCP allows you to equip agent mode with the context and capabilities it needs to help you, like a USB port for intelligence. When you enter a chat prompt in agent mode within VS Code, the model can use different tools to handle tasks like understanding database schema or querying the web. Enhancing AI Integrations with MCP and Azure API Management Enhance AI integrations using MCP and Azure API Management Understanding and Mitigating Security Risks in MCP Implementations Overview of security risks and mitigation strategies for MCP implementations Protecting Against Indirect Injection Attacks in MCP Strategies to prevent indirect injection attacks in MCP implementations Microsoft Copilot Studio MCP Announcement of the Microsoft Copilot Studio MCP lab Getting started with MCP for Beginners 9 part course on MCP Client and Servers Code Repositories Try it Yourself Want to build something with MCP? Microsoft has shared open-source sample code in Python, .NET, and TypeScript: Repo Name Language Description Azure-Samples/remote-mcp-apim-functions-python Python Recommended for Secure remote hosting Sample Python Azure Functions demonstrating remote MCP integration with Azure API Management Azure-Samples/remote-mcp-functions-python Python Sample Python Azure Functions demonstrating remote MCP integration Azure-Samples/remote-mcp-functions-dotnet C# Sample .NET Azure Functions demonstrating remote MCP integration Azure-Samples/remote-mcp-functions-typescript TypeScript Sample TypeScript Azure Functions demonstrating remote MCP integration Microsoft Copilot Studio MCP TypeScript Microsoft Copilot Studio MCP lab You can clone the repo, open it in VS Code, and follow the instructions to run your own MCP server. Using MCP with the AI Toolkit in Visual Studio Code To make your MCP journey even easier, Microsoft provides the AI Toolkit for Visual Studio Code. This toolkit includes: A built-in model catalog Tools to help you deploy and run models locally Seamless integration with MCP agent tools You can install the AI Toolkit extension from the Visual Studio Code Marketplace. Once installed, it helps you: Discover and select models quickly Connect those models to MCP agents Develop and test AI workflows locally before deploying to the cloud You can explore the full documentation here: Overview of the AI Toolkit for Visual Studio Code – Microsoft Learn This is perfect for developers who want to test things on their own system without needing a cloud setup right away. Why Should You Care About MCP? Because MCP: Makes your AI tools more powerful by giving them real-time knowledge Works with GitHub Copilot, Azure, and VS Code tools you may already use Is open-source and beginner-friendly with lots of tutorials and sample code It’s the future of AI development connecting models to the real world. Final Thoughts If you're learning AI or building software agents, don’t miss this valuable MCP Resources Hub. It’s like a starter kit for building smart, connected agents with Microsoft tools. Try one video or repo today. Experiment. Learn by doing and start your journey with the MCP for Beginners curricula.Step-by-Step: How to Setup Copilot Chat in VS Code
Copilot Chat is an AI-powered chatbot leveraging OpenAI's GPT-4, designed to enhance your coding workflow. Learn how to set up Copilot Chat step by step in Visual Studio Code (VS Code). Benefit from personalized and flexible coding environments, code analysis, automated unit test generation, and bug fixes. Prerequisites include an active GitHub account and the latest version of VS Code. Elevate your coding efficiency to new heights with Copilot Chat.How to use any Python AI agent framework with free GitHub Models
I ❤️ when companies offer free tiers for developer services, since it gives everyone a way to learn new technologies without breaking the bank. Free tiers are especially important for students and people between jobs, when the desire to learn is high but the available cash is low. That's why I'm such a fan of GitHub Models: free, high-quality generative AI models available to anyone with a GitHub account. The available models include the latest OpenAI LLMs (like o3-mini), LLMs from the research community (like Phi and Llama), LLMs from other popular providers (like Mistral and Jamba), multimodal models (like gpt-4o and llama-vision-instruct) and even a few embedding models (from OpenAI and Cohere). With access to such a range of models, you can prototype complex multi-model workflows to improve your productivity or heck, just make something fun for yourself. 🤗 To use GitHub Models, you can start off in no-code mode: open the playground for a model, send a few requests, tweak the parameters, and check out the answers. When you're ready to write code, select "Use this model". A screen will pop up where you can select a programming language (Python/JavaScript/C#/Java/REST) and select an SDK (which varies depending on model). Then you'll get instructions and code for that model, language, and SDK. But here's what's really cool about GitHub Models: you can use them with all the popular Python AI frameworks, even if the framework has no specific integration with GitHub Models. How is that possible? The vast majority of Python AI frameworks support the OpenAI Chat Completions API, since that API became a defacto standard supported by many LLM API providers besides OpenAI itself. GitHub Models also provide OpenAI-compatible endpoints for chat completion models. Therefore, any Python AI framework that supports OpenAI-like models can be used with GitHub Models as well. 🎉 To prove it, I've made a new repository with examples from eight different Python AI agent packages, all working with GitHub Models: python-ai-agent-frameworks-demos. There are examples for AutoGen, LangGraph, Llamaindex, OpenAI Agents SDK, OpenAI standard SDK, PydanticAI, Semantic Kernel, and SmolAgents. You can open that repository in GitHub Codespaces, install the packages, and get the examples running immediately. Now let's walk through the API connection code for GitHub Models for each framework. Even if I missed your favorite framework, I hope my tips here will help you connect any framework to GitHub Models. OpenAI I'll start with openai , the package that started it all! import openai client = openai.OpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") The code above demonstrates the two key parameters we'll need to configure for all frameworks: api_key : When using OpenAI.com, you pass your OpenAI API key here. When using GitHub Models, you pass in a Personal Access Token (PAT). If you open the repository (or any repository) in GitHub Codespaces, a PAT is already stored in the GITHUB_TOKEN environment variable. However, if you're working locally with GitHub Models, you'll need to generate a PAT yourself and store it. PATs expire after a while, so you need to generate new PATs every so often. base_url : This parameter tells the OpenAI client to send all requests to "https://models.inference.ai.azure.com" instead of the OpenAI.com API servers. That's the domain that hosts the OpenAI-compatible endpoint for GitHub Models, so you'll always pass that domain as the base URL. If we're working with the new openai-agents SDK, we use very similar code, but we must use the AsyncOpenAI client from openai instead. Lately, Python AI packages are defaulting to async, because it's so much better for performance. import agents import openai client = openai.AsyncOpenAI( base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"]) model = agents.OpenAIChatCompletionsModel( model="gpt-4o", openai_client=client) spanish_agent = agents.Agent( name="Spanish agent", instructions="You only speak Spanish.", model=model) PydanticAI Now let's look at all of the packages that make it really easy for us, by allowing us to directly bring in an instance of either OpenAI or AsyncOpenAI . For PydanticAI, we configure an AsyncOpenAI client, then construct an OpenAIModel object from PydanticAI, and pass that model to the agent: import openai import pydantic_ai import pydantic_ai.models.openai client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") model = pydantic_ai.models.openai.OpenAIModel( "gpt-4o", provider=OpenAIProvider(openai_client=client)) spanish_agent = pydantic_ai.Agent( model, system_prompt="You only speak Spanish.") Semantic Kernel For Semantic Kernel, the code is very similar. We configure an AsyncOpenAI client, then construct an OpenAIChatCompletion object from Semantic Kernel, and add that object to the kernel. import openai import semantic_kernel.connectors.ai.open_ai import semantic_kernel.agents chat_client = openai.AsyncOpenAI( api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") chat = semantic_kernel.connectors.ai.open_ai.OpenAIChatCompletion( ai_model_id="gpt-4o", async_client=chat_client) kernel.add_service(chat) spanish_agent = semantic_kernel.agents.ChatCompletionAgent( kernel=kernel, name="Spanish agent" instructions="You only speak Spanish") AutoGen Next, we'll check out a few frameworks that have their own wrapper of the OpenAI clients, so we won't be using any classes from openai directly. For AutoGen, we configure both the OpenAI parameters and the model name in the same object, then pass that to each agent: import autogen_ext.models.openai import autogen_agentchat.agents client = autogen_ext.models.openai.OpenAIChatCompletionClient( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com") spanish_agent = autogen_agentchat.agents.AssistantAgent( "spanish_agent", model_client=client, system_message="You only speak Spanish") LangGraph For LangGraph, we configure a very similar object, which even has the same parameter names: import langchain_openai import langgraph.graph model = langchain_openai.ChatOpenAI( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], base_url="https://models.inference.ai.azure.com", ) def call_model(state): messages = state["messages"] response = model.invoke(messages) return {"messages": [response]} workflow = langgraph.graph.StateGraph(MessagesState) workflow.add_node("agent", call_model) SmolAgents Once again, for SmolAgents, we configure a similar object, though with slightly different parameter names: import smolagents model = smolagents.OpenAIServerModel( model_id="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = smolagents.CodeAgent(model=model) Llamaindex I saved Llamaindex for last, as it is the most different. The llama-index package has a different constructor for OpenAI.com versus OpenAI-like servers, so I opted to use that OpenAILike constructor instead. However, I also needed an embeddings model for my example, and the package doesn't have an OpenAIEmbeddingsLike constructor, so I used the standard OpenAIEmbedding constructor. import llama_index.embeddings.openai import llama_index.llms.openai_like import llama_index.core.agent.workflow Settings.llm = llama_index.llms.openai_like.OpenAILike( model="gpt-4o", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com", is_chat_model=True) Settings.embed_model = llama_index.embeddings.openai.OpenAIEmbedding( model="text-embedding-3-small", api_key=os.environ["GITHUB_TOKEN"], api_base="https://models.inference.ai.azure.com") agent = llama_index.core.agent.workflow.ReActAgent( tools=query_engine_tools, llm=Settings.llm) Choose your models wisely! In all of the examples above, I specified the gpt-4o model. The gpt-4o model is a great choice for agents because it supports function calling, and many agent frameworks only work (or work best) with models that natively support function calling. Fortunately, GitHub Models includes multiple models that support function calling, at least in my basic experiments: gpt-4o gpt-4o-mini o3-mini AI21-Jamba-1.5-Large AI21-Jamba-1.5-Mini Codestral-2501 Cohere-command-r Ministral-3B Mistral-Large-2411 Mistral-Nemo Mistral-small You might find that some models work better than others, especially if you're using agents with multiple tools. With GitHub Models, it's very easy to experiment and see for yourself, by simply changing the model name and re-running the code. Join the AI Agents Hackathon We are currently running a free virtual hackathon from April 8th - 30th, to challenge developers to create agentic applications using Microsoft technologies. You could build an agent entirely using GitHub Models and submit it to the hackathon for a chance to win amazing prizes! You can also join our 30+ streams about building AI agents, including a stream all about prototyping with GitHub Models. Learn more and register at https://aka.ms/agentshackRedeeming Azure for Student from your GitHub Student Pack when you do not have an Academic Email
GitHub Student Developer Pack Learn to ship software like a pro. There's no substitute for hands-on experience. But for most students, real world tools can be cost-prohibitive. That's why we created the GitHub Student Developer Pack with some of our partners and friends. Sign up for Student Developer PackDeploy Your First App Using GitHub Copilot for Azure: A Beginner’s Guide
Deploying an app for the first time can feel overwhelming. You may find yourself switching between tutorials, scanning documentation, and wondering if you missed a step. But what if you could do it all in one place? Now you can! With GitHub Copilot for Azure, you can receive real time deployment guidance without leaving the Visual Studio Code. While it won’t fully automate deployments, it serves as a step-by-step AI powered assistant, helping you navigate the process with clear, actionable instructions. No more endless tab switching or searching for the right tutorial—simply type, deploy, and learn, all within your IDE i.e. Visual Studio Code. If you are a student, you have access to exclusive opportunities! Whether you are exploring new technologies or experimenting with them, platforms like GitHub Education and the Microsoft Learn Student Hub provide free Azure credits, structured learning paths, and certification opportunities. These resources can help you gain hands-on experience with GitHub Copilot for Azure and streamline your journey toward deploying applications efficiently. Prerequisites: Before we begin, ensure you have the following: Account in GitHub. Sign up with GitHub Copilot. Account in Azure (Claim free credits using Azure for Students) Visual Studio Code installed. Step 1: Installation How to install GitHub Copilot for Azure? Open VS Code, in the leftmost panel, click on Extensions, type – ‘GitHub Copilot for Azure’, and install the first result which is by Microsoft. After this installation, you will be prompted to install – GitHub Copilot, Azure Tools, and other required installations. Click on allow and install all required extensions from the same method, as used above. Step 2: Enable How to enable GitHub Copilot in GitHub? Open GitHub click on top rightmost Profile pic, a left panel will open. Click on Your Copilot. Upon opening, enable it for IDE, as shown in the below Figure. Step 3: Walkthrough Open VSCode, and click on the GitHub Copilot icon from topmost right side. This will open the GitHub Copilot Chat. From here, you can customize the model type and Send commands. Type azure to work with Azure related tasks. Below figure will help to locate the things smoothly: Step 4: Generate Boilerplate Code with GitHub Copilot Let’s start by creating a simple HTML website that we will deploy to Azure Static Web Apps Service. Prompt for GitHub Copilot: Create a simple "Hello, World!" code with HTML. Copilot will generate a basic structure like this: Then, click on "Edit with Copilot." It will create an index.html file and add the code to it. Then, click on "Accept" and modify the content and style if needed before moving forward. Step 5: Deploy Your App Using Copilot Prompts Instead of searching for documentation, let’s use Copilot to generate deployment instructions directly within Visual Studio Code. Trigger Deployment Prompts Using azure To get deployment related suggestions, use azure in GitHub Copilot’s chat. In the chat text box at the bottom of the pane, type the following prompt after azure, then select Send (paper airplane icon) or press Enter on your keyboard: Prompt: azure How do I deploy a static website? Copilot will provide two options: deploying via Azure Blob Storage or Azure Static Web App Service. We will proceed with Azure Static Web Apps, so we will ask Copilot to guide us through deploying our app using this service. We will use the following prompt: azure I would like to deploy a site using Azure Static Web Apps. Please provide a step-by-step guide. Copilot will then return steps like: You will receive a set of instructions to deploy your website. To make it simpler, you can ask Copilot for a more detailed guide. To get a detailed guide, we will use the following prompt: azure Can you provide a more detailed guide and elaborate on GitHub Actions, including the steps to take for GitHub Actions? Copilot will then return steps like: See? That’s how you can experiment, ask questions, and get step-by-step guidance. Remember, the better the prompt, the better the results will be. Step 6: Learn as You Deploy One of the best features of Copilot is that you can ask follow-up questions if anything is unclear—all within Visual Studio Code, without switching tabs. Examples of Useful Prompts: What Azure services should I use with my app? What is GitHub Actions, and how does it work? What are common issues when deploying to Azure, and how can I fix them? Copilot provides contextual responses, guiding you through troubleshooting and best practices. You can learn more about this here. Conclusion: With GitHub Copilot for Azure, deploying applications is now more intuitive than ever. Instead of memorizing complex commands, you can use AI powered prompts to generate deployment steps in real time and even debug the errors within Visual Studio Code. 🚀 Next Steps: Experience with different prompts and explore how Copilot assists you. Try deploying more advanced applications, like Node.js or Python apps. GitHub Copilot isn’t just an AI assistant, it’s a learning tool. The more you engage with it, the more confident you’ll become in deploying and managing applications on Azure! Learn more about GitHub Copilot for Azure: Understand what GitHub Copilot for Azure Preview is and how it works. See example prompts for learning more about Azure and understanding your Azure account, subscription, and resources. See example prompts for designing and developing applications for Azure. See example prompts for deploying your application to Azure. See example prompts for optimizing your applications in Azure. See example prompts for troubleshooting your Azure resources. That's it, folks! But the best part? You can become part of a thriving community of learners and builders by joining the Microsoft Learn Student Ambassadors Community. Connect with like-minded individuals, explore hands-on projects, and stay updated with the latest in cloud and AI. 💬 Join the community on Discord here.Exploring Azure OpenAI Assistants and Azure AI Agent Services: Benefits and Opportunities
In the rapidly evolving landscape of artificial intelligence, businesses are increasingly turning to cloud-based solutions to harness the power of AI. Microsoft Azure offers two prominent services in this domain: Azure OpenAI Assistants and Azure AI Agent Services. While both services aim to enhance user experiences and streamline operations, they cater to different needs and use cases. This blog post will delve into the details of each service, their benefits, and the opportunities they present for businesses. Understanding Azure OpenAI Assistants What Are Azure OpenAI Assistants? Azure OpenAI Assistants are designed to leverage the capabilities of OpenAI's models, such as GPT-3 and its successors. These assistants are tailored for applications that require advanced natural language processing (NLP) and understanding, making them ideal for conversational agents, chatbots, and other interactive applications. Key Features Pre-trained Models: Azure OpenAI Assistants utilize pre-trained models from OpenAI, which means they come with a wealth of knowledge and language understanding out of the box. This reduces the time and effort required for training models from scratch. Customizability: While the models are pre-trained, developers can fine-tune them to meet specific business needs. This allows for the creation of personalized experiences that resonate with users. Integration with Azure Ecosystem: Azure OpenAI Assistants seamlessly integrate with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Cognitive Services. This enables businesses to build comprehensive solutions that leverage multiple Azure capabilities. Benefits of Azure OpenAI Assistants Enhanced User Experience: By utilizing advanced NLP capabilities, Azure OpenAI Assistants can provide more natural and engaging interactions. This leads to improved customer satisfaction and loyalty. Rapid Deployment: The availability of pre-trained models allows businesses to deploy AI solutions quickly. This is particularly beneficial for organizations looking to implement AI without extensive development time. Scalability: Azure's cloud infrastructure ensures that applications built with OpenAI Assistants can scale to meet growing user demands without compromising performance. Understanding Azure AI Agent Services What Are Azure AI Agent Services? Azure AI Agent Services provide a more flexible framework for building AI-driven applications. Unlike Azure OpenAI Assistants, which are limited to OpenAI models, Azure AI Agent Services allow developers to utilize a variety of AI models, including those from other providers or custom-built models. Key Features Model Agnosticism: Developers can choose from a wide range of AI models, enabling them to select the best fit for their specific use case. This flexibility encourages innovation and experimentation. Custom Agent Development: Azure AI Agent Services support the creation of custom agents that can perform a variety of tasks, from simple queries to complex decision-making processes. Integration with Other AI Services: Like OpenAI Assistants, Azure AI Agent Services can integrate with other Azure services, allowing for the creation of sophisticated AI solutions that leverage multiple technologies. Benefits of Azure AI Agent Services Diverse Use Cases: The ability to use any AI model opens a world of possibilities for businesses. Whether it's a specialized model for sentiment analysis or a custom-built model for a niche application, organizations can tailor their solutions to meet specific needs. Enhanced Automation: AI agents can automate repetitive tasks, freeing up human resources for more strategic activities. This leads to increased efficiency and productivity. Cost-Effectiveness: By allowing the use of various models, businesses can choose cost-effective solutions that align with their budget and performance requirements. Opportunities for Businesses Improved Customer Engagement Both Azure OpenAI Assistants and Azure AI Agent Services can significantly enhance customer engagement. By providing personalized and context-aware interactions, businesses can create a more satisfying user experience. For example, a retail company can use an AI assistant to provide tailored product recommendations based on customer preferences and past purchases. Data-Driven Decision Making AI agents can analyze vast amounts of data and provide actionable insights. This capability enables organizations to make informed decisions based on real-time data analysis. For instance, a financial institution can deploy an AI agent to monitor market trends and provide investment recommendations to clients. Streamlined Operations By automating routine tasks, businesses can streamline their operations and reduce operational costs. For example, a customer support team can use AI agents to handle common inquiries, allowing human agents to focus on more complex issues. Innovation and Experimentation The flexibility of Azure AI Agent Services encourages innovation. Developers can experiment with different models and approaches to find the most effective solutions for their specific challenges. This culture of experimentation can lead to breakthroughs in product development and service delivery. Enhanced Analytics and Insights Integrating AI agents with analytics tools can provide businesses with deeper insights into customer behavior and preferences. This data can inform marketing strategies, product development, and customer service improvements. For example, a company can analyze interactions with an AI assistant to identify common customer pain points, allowing them to address these issues proactively. Conclusion In summary, both Azure OpenAI Assistants and Azure AI Agent Services offer unique advantages that can significantly benefit businesses looking to leverage AI technology. Azure OpenAI Assistants provide a robust framework for building conversational agents using advanced OpenAI models, making them ideal for applications that require sophisticated natural language understanding and generation. Their ease of integration, rapid deployment, and enhanced user experience make them a compelling choice for businesses focused on customer engagement. Azure AI Agent Services, on the other hand, offer unparalleled flexibility by allowing developers to utilize a variety of AI models. This model-agnostic approach encourages innovation and experimentation, enabling businesses to tailor solutions to their specific needs. The ability to automate tasks and streamline operations can lead to significant cost savings and increased efficiency. Additional Resources To further explore Azure OpenAI Assistants and Azure AI Agent Services, consider the following resources: Agent Service on Microsoft Learn Docs Watch On-Demand Sessions Streamlining Customer Service with AI-Powered Agents: Building Intelligent Multi-Agent Systems with Azure AI Microsoft learn Develop AI agents on Azure - Training | Microsoft Learn Community and Announcements Tech Community Announcement: Introducing Azure AI Agent Service Bonus Blog Post: Announcing the Public Preview of Azure AI Agent Service AI Agents for Beginners 10 Lesson Course https://aka.ms/ai-agents-beginners
How to Optimize your Codespaces: Pro-tips for managing quotas
Now that GitHub Codespaces is free for anyone, you might be surprised to see how fast you can hit the free quota. Here are four things you can do to make the most out of the 90 hours you get every month (and 180 hours if you are a student).
