Study Buddy: Learning Data Science and Machine Learning with an AI Sidekick
If you've ever wished for a friendly companion to guide you through the world of data science and machine learning, you're not alone. As part of the "For Beginners" curriculum, I recently built a Study Buddy Agent, an AI-powered assistant designed to help learners explore data science interactively, intuitively, and joyfully. Why a Study Buddy? Learning something new can be overwhelming, especially when you're navigating complex topics like machine learning, statistics, or Python programming. The Study Buddy Agent is here to change that. It brings the curriculum to life by answering questions, offering explanations, and nudging learners toward deeper understanding, all in a conversational format. Think of it as your AI-powered lab partner: always available, never judgmental, and endlessly curious. Built with chatmodes, Powered by Purpose The agent lives inside a .chatmodes file in the https://github.com/microsoft/Data-Science-For-Beginners/blob/main/.github/chatmodes/study-mode.chatmode.md. This file defines how the agent behaves, what tone it uses, and how it interacts with learners. I designed it to be friendly, encouraging, and beginner-first—just like the curriculum itself. It’s not just about answering questions. The Study Buddy is trained to: Reinforce key concepts from the curriculum Offer hints and nudges when learners get stuck Encourage exploration and experimentation Celebrate progress and milestones What’s Under the Hood? The agent uses GitHub Copilot's chatmode, which allows developers to define custom behaviors for AI agents. By aligning the agent’s responses with the curriculum’s learning objectives, we ensure that learners stay on track while enjoying the flexibility of conversational learning. How You Can Use It YouTube Video here: Study Buddy - Data Science AI Sidekick Clone the repo: Head to the https://github.com/microsoft/Data-Science-For-Beginners and clone it locally or use Codespaces. Open the GitHub Copilot Chat, and select Study Buddy: This will activate the Study Buddy. Start chatting: Ask questions, explore topics, and let the agent guide you. What’s Next? This is just the beginning. I’m exploring ways to: Expand the agent to other beginner curriculums (Web Dev, AI, IoT) Integrate feedback loops so learners can shape the agent’s evolution Final Thoughts In my role, I believe learning should be inclusive, empowering, and fun. The Study Buddy Agent is a small step toward that vision, a way to make data science feel less like a mountain and more like a hike with a good friend. Try it out, share your feedback, and let’s keep building tools that make learning magical. Join us on Discord to share your feedback.Introducing the Microsoft Agent Framework
Introducing the Microsoft Agent Framework: A Unified Foundation for AI Agents and Workflows The landscape of AI development is evolving rapidly, and Microsoft is at the forefront with the release of the Microsoft Agent Framework an open-source SDK designed to empower developers to build intelligent, multi-agent systems with ease and precision. Whether you're working in .NET or Python, this framework offers a unified, extensible foundation that merges the best of Semantic Kernel and AutoGen, while introducing powerful new capabilities for agent orchestration and workflow design. Introducing Microsoft Agent Framework: The Open-Source Engine for Agentic AI Apps | Azure AI Foundry Blog Introducing Microsoft Agent Framework | Microsoft Azure Blog Why Another Agent Framework? Both Semantic Kernel and AutoGen have pioneered agentic development, Semantic Kernel with its enterprise-grade features and AutoGen with its research-driven abstractions. The Microsoft Agent Framework is the next generation of both, built by the same teams to unify their strengths: AutoGen’s simplicity in multi-agent orchestration. Semantic Kernel’s robustness in thread-based state management, telemetry, and type safety. New capabilities like graph-based workflows, checkpointing, and human-in-the-loop support This convergence means developers no longer have to choose between experimentation and production. The Agent Framework is designed to scale from single-agent prototypes to complex, enterprise-ready systems Core Capabilities AI Agents AI agents are autonomous entities powered by LLMs that can process user inputs, make decisions, call tools and MCP servers, and generate responses. They support providers like Azure OpenAI, OpenAI, and Azure AI, and can be enhanced with: Agent threads for state management. Context providers for memory. Middleware for action interception. MCP clients for tool integration Use cases include customer support, education, code generation, research assistance, and more—especially where tasks are dynamic and underspecified. Workflows Workflows are graph-based orchestrations that connect multiple agents and functions to perform complex, multi-step tasks. They support: Type-based routing Conditional logic Checkpointing Human-in-the-loop interactions Multi-agent orchestration patterns (sequential, concurrent, hand-off, Magentic) Workflows are ideal for structured, long-running processes that require reliability and modularity. Developer Experience The Agent Framework is designed to be intuitive and powerful: Installation: Python: pip install agent-framework .NET: dotnet add package Microsoft.Agents.AI Integration: Works with Foundry SDK, MCP SDK, A2A SDK, and M365 Copilot Agents Samples and Manifests: Explore declarative agent manifests and code samples Learning Resources: Microsoft Learn modules AI Agents for Beginners AI Show demos Azure AI Foundry Discord community Migration and Compatibility If you're currently using Semantic Kernel or AutoGen, migration guides are available to help you transition smoothly. The framework is designed to be backward-compatible where possible, and future updates will continue to support community contributions via the GitHub repository. Important Considerations The Agent Framework is in public preview. Feedback and issues are welcome on the GitHub repository. When integrating with third-party servers or agents, review data sharing practices and compliance boundaries carefully. The Microsoft Agent Framework marks a pivotal moment in AI development, bringing together research innovation and enterprise readiness into a single, open-source foundation. Whether you're building your first agent or orchestrating a fleet of them, this framework gives you the tools to do it safely, scalably, and intelligently. Ready to get started? Download the SDK, explore the documentation, and join the community shaping the future of AI agents.From Cloud to Chip: Building Smarter AI at the Edge with Windows AI PCs
As AI engineers, we’ve spent years optimizing models for the cloud, scaling inference, wrangling latency, and chasing compute across clusters. But the frontier is shifting. With the rise of Windows AI PCs and powerful local accelerators, the edge is no longer a constraint it’s now a canvas. Whether you're deploying vision models to industrial cameras, optimizing speech interfaces for offline assistants, or building privacy-preserving apps for healthcare, Edge AI is where real-world intelligence meets real-time performance. Why Edge AI, Why Now? Edge AI isn’t just about running models locally, it’s about rethinking the entire lifecycle: - Latency: Decisions in milliseconds, not round-trips to the cloud. - Privacy: Sensitive data stays on-device, enabling HIPAA/GDPR compliance. - Resilience: Offline-first apps that don’t break when the network does. - Cost: Reduced cloud compute and bandwidth overhead. With Windows AI PCs powered by Intel and Qualcomm NPUs and tools like ONNX Runtime, DirectML, and Olive, developers can now optimize and deploy models with unprecedented efficiency. What You’ll Learn in Edge AI for Beginners The Edge AI for Beginners curriculum is a hands-on, open-source guide designed for engineers ready to move from theory to deployment. Multi-Language Support This content is available in over 48 languages, so you can read and study in your native language. What You'll Master This course takes you from fundamental concepts to production-ready implementations, covering: Small Language Models (SLMs) optimized for edge deployment Hardware-aware optimization across diverse platforms Real-time inference with privacy-preserving capabilities Production deployment strategies for enterprise applications Why EdgeAI Matters Edge AI represents a paradigm shift that addresses critical modern challenges: Privacy & Security: Process sensitive data locally without cloud exposure Real-time Performance: Eliminate network latency for time-critical applications Cost Efficiency: Reduce bandwidth and cloud computing expenses Resilient Operations: Maintain functionality during network outages Regulatory Compliance: Meet data sovereignty requirements Edge AI Edge AI refers to running AI algorithms and language models locally on hardware, close to where data is generated without relying on cloud resources for inference. It reduces latency, enhances privacy, and enables real-time decision-making. Core Principles: On-device inference: AI models run on edge devices (phones, routers, microcontrollers, industrial PCs) Offline capability: Functions without persistent internet connectivity Low latency: Immediate responses suited for real-time systems Data sovereignty: Keeps sensitive data local, improving security and compliance Small Language Models (SLMs) SLMs like Phi-4, Mistral-7B, Qwen and Gemma are optimized versions of larger LLMs, trained or distilled for: Reduced memory footprint: Efficient use of limited edge device memory Lower compute demand: Optimized for CPU and edge GPU performance Faster startup times: Quick initialization for responsive applications They unlock powerful NLP capabilities while meeting the constraints of: Embedded systems: IoT devices and industrial controllers Mobile devices: Smartphones and tablets with offline capabilities IoT Devices: Sensors and smart devices with limited resources Edge servers: Local processing units with limited GPU resources Personal Computers: Desktop and laptop deployment scenarios Course Modules & Navigation Course duration. 10 hours of content Module Topic Focus Area Key Content Level Duration 📖 00 Introduction to EdgeAI Foundation & Context EdgeAI Overview • Industry Applications • SLM Introduction • Learning Objectives Beginner 1-2 hrs 📚 01 EdgeAI Fundamentals Cloud vs Edge AI comparison EdgeAI Fundamentals • Real World Case Studies • Implementation Guide • Edge Deployment Beginner 3-4 hrs 🧠 02 SLM Model Foundations Model families & architecture Phi Family • Qwen Family • Gemma Family • BitNET • μModel • Phi-Silica Beginner 4-5 hrs 🚀 03 SLM Deployment Practice Local & cloud deployment Advanced Learning • Local Environment • Cloud Deployment Intermediate 4-5 hrs ⚙️ 04 Model Optimization Toolkit Cross-platform optimization Introduction • Llama.cpp • Microsoft Olive • OpenVINO • Apple MLX • Workflow Synthesis Intermediate 5-6 hrs 🔧 05 SLMOps Production Production operations SLMOps Introduction • Model Distillation • Fine-tuning • Production Deployment Advanced 5-6 hrs 🤖 06 AI Agents & Function Calling Agent frameworks & MCP Agent Introduction • Function Calling • Model Context Protocol Advanced 4-5 hrs 💻 07 Platform Implementation Cross-platform samples AI Toolkit • Foundry Local • Windows Development Advanced 3-4 hrs 🏭 08 Foundry Local Toolkit Production-ready samples Sample applications (see details below) Expert 8-10 hrs Each module includes Jupyter notebooks, code samples, and deployment walkthroughs, perfect for engineers who learn by doing. Developer Highlights - 🔧 Olive: Microsoft's optimization toolchain for quantization, pruning, and acceleration. - 🧩 ONNX Runtime: Cross-platform inference engine with support for CPU, GPU, and NPU. - 🎮 DirectML: GPU-accelerated ML API for Windows, ideal for gaming and real-time apps. - 🖥️ Windows AI PCs: Devices with built-in NPUs for low-power, high-performance inference. Local AI: Beyond the Edge Local AI isn’t just about inference, it’s about autonomy. Imagine agents that: - Learn from local context - Adapt to user behavior - Respect privacy by design With tools like Agent Framework, Azure AI Foundry and Windows Copilot Studio, and Foundry Local developers can orchestrate local agents that blend LLMs, sensors, and user preferences, all without cloud dependency. Try It Yourself Ready to get started? Clone the Edge AI for Beginners GitHub repo, run the notebooks, and deploy your first model to a Windows AI PC or IoT devices Whether you're building smart kiosks, offline assistants, or industrial monitors, this curriculum gives you the scaffolding to go from prototype to production.Essential Microsoft Resources for MVPs & the Tech Community from the AI Tour
Unlock the power of Microsoft AI with redeliverable technical presentations, hands-on workshops, and open-source curriculum from the Microsoft AI Tour! Whether you’re a Microsoft MVP, Developer, or IT Professional, these expertly crafted resources empower you to teach, train, and lead AI adoption in your community. Explore top breakout sessions covering GitHub Copilot, Azure AI, Generative AI, and security best practices—designed to simplify AI integration and accelerate digital transformation. Dive into interactive workshops that provide real-world applications of AI technologies. Take it a step further with Microsoft’s Open-Source AI Curriculum, offering beginner-friendly courses on AI, Machine Learning, Data Science, Cybersecurity, and GitHub Copilot—perfect for upskilling teams and fostering innovation. Don’t just learn—lead. Access these resources, host impactful training sessions, and drive AI adoption in your organization. Start sharing today! Explore now: Microsoft AI Tour Resources.How to Master GitHub Copilot: Build, Prompt, Deploy Smarter
Mastering GitHub Copilot: Build, Prompt, Deploy Smarter is a free, hands-on workshop designed to help developers go beyond autocomplete and unlock the true power of AI-assisted coding. Instead of toy examples, this course walks you through real-world software engineering challenges: messy codebases, multi-language projects, cloud deployments, and legacy system upgrades. You’ll learn practical skills like prompt engineering, advanced Copilot features, and AI pair programming techniques that make you faster, sharper, and more creative. Whether you’re a junior developer or a seasoned architect, mastering GitHub Copilot will help you: Reduce cognitive load and focus on system design Accelerate onboarding for new engineers Write cleaner, more consistent code Automate repetitive tasks to free up time for innovation AI coding tools like GitHub Copilot are no longer optional—they’re essential. This workshop gives you the skills to collaborate with Copilot effectively and stay competitive in the age of AI-powered development.Use Copilot and MCP to query Microsoft Learn Docs
Are you ready to take your Azure development workflow to the next level? In this post, we’ll walk through how to use GitHub Copilot in Agent Mode—paired with MCP (Model Context Protocol) servers—to get trusted, grounded answers from Microsoft Learn Docs, right inside your coding workspace. Whether you’re tired of switching tabs to search documentation or want to ensure your AI assistant’s answers are always accurate, this guide will show you how to streamline your workflow and boost your productivity.One MCP Server, Two Transports: STDIO and HTTP
Let's think about a situation for using MCP servers. Most MCP servers run on a local machine – either directly or in a container. But with other integration scenarios like using Copilot Studio, enterprise-wide MCP servers or need more secure environments, those MCP server should run remotely through HTTP. As long as the core logic lives in a shared layer, wrapping it in a console (STDIO) or web (HTTP) host is straightforward. However, maintaining two hosts can duplicate code. What if a single MCP server supports both STDIO and HTTP, controlled by a simple switch? It will be reducing significant amount of management overhead. This post shows how to build a single MCP server that supports both transports, selected at runtime with a --http switch, using the .NET builder pattern. .NET Builder Pattern A .NET console app starts the builder pattern using Host.CreateApplicationBuilder(args) . var builder = Host.CreateApplicationBuilder(args); The builder instance is the type of HostApplicationBuilder implementing the IHostApplicationBuilder interface. On the other hand, an ASP.NET web app starts the builder pattern using WebApplication.CreateBuilder(args) . var builder = WebApplication.CreateBuilder(args); This builder instance is the type of WebApplicationBuilder also implementing the IHostApplicationBuilder interface. Now, both builder instances have IHostApplicationBuilder in common, and this is the key of this post today. If we decide the hosting mode before creating the builder instance, the server can run as either STDIO or HTTP. The --http Switch as an Argument As you can see, both Host.CreateApplicationBuilder(args) and WebApplication.CreateBuilder(args) take the list of arguments that are passed from the command-line. Therefore, before initializing the builder instance, we can identify the server type. Let's use a --http switch as the selector. Then pass --http when running the server. dotnet run --project MyMcpServer -- --http Then, before creating the builder instance, check whether the switch is present. It looks for the environment variables first, then checks the arguments passed. public static bool UseStreamableHttp(IDictionary env, string[] args) { var useHttp = env.Contains("UseHttp") && bool.TryParse(env["UseHttp"]?.ToString()?.ToLowerInvariant(), out var result) && result; if (args.Length == 0) { return useHttp; } useHttp = args.Contains("--http", StringComparer.InvariantCultureIgnoreCase); return useHttp; } Here's the usage: var useStreamableHttp = UseStreamableHttp(Environment.GetEnvironmentVariables(), args); We've identified whether to use HTTP or not. Therefore, the builder instance is built in this way: IHostApplicationBuilder builder = useStreamableHttp ? WebApplication.CreateBuilder(args) : Host.CreateApplicationBuilder(args); With this builder instance, we can add more dependencies specific to web app or console app depending on the scenario. The Transport Type Let's add the MCP server to the builder instance. var mcpServerBuilder = builder.Services.AddMcpServer() .WithPromptsFromAssembly() .WithResourcesFromAssembly() .WithToolsFromAssembly(); We haven’t told mcpServerBuilder which transport to use yet. Use useStreamableHttp to select the transport. if (useStreamableHttp) { mcpServerBuilder.WithHttpTransport(o => o.Stateless = true); } else { mcpServerBuilder.WithStdioServerTransport(); } Type Casting to Run Server While configuring an ASP.NET web app, middlewares are added. The HTTP host also needs middleware, and the builder must be cast. After the builder instance is built, the webApp instance adds middleware including the endpoint mapping. IHost app; if (useStreamableHttp) { var webApp = (builder as WebApplicationBuilder)!.Build(); webApp.UseHttpsRedirection(); webApp.MapMcp("/mcp"); app = webApp; } else { var consoleApp = (builder as HostApplicationBuilder)!.Build(); app = consoleApp; } Note that WebApplication implements IHost, so you can assign it to an IHost variable. The console host built from HostApplicationBuilder is already an IHost. Use this app instance to run the MCP server. await app.RunAsync(); That's it! Now you can run the MCP server with the STDIO transport or the HTTP transport by providing a single switch, --http . Sample apps Sample apps are available for you to check out. Visit the MCP Samples in .NET repository, and you'll find MCP server apps. All server apps in the repo support both STDIO and HTTP via the switch. More resources If you'd like to learn more about MCP in .NET, here are some additional resources worth exploring: Let's Learn MCP MCP Workshop in .NET MCP Samples in .NET MCP Samples MCP for BeginnersMCP Bootcamp: APAC, LATAM and Brazil
The Model Context Protocol (MCP) is transforming how AI systems interact with real-world applications. From intelligent assistants to real-time streaming, MCP is already being adopted by leading companies—and now is your chance to get ahead. Join us for a four-part technical series designed to give you practical, production-ready skills in MCP development, integration, and deployment. Whether you're a developer, AI engineer, or cloud architect, this series will equip you with the tools to build and scale MCP-based solutions. 📅 English edition - 6PM IST (India Standard Time) ✅ Register at MCP Bootcamp APAC Session Title Date & Time (IST) Creating Your First MCP Server Learn the fundamental concepts of the protocol and test your implementation using official tools. August 28, 6:00 PM MCP Integration with LLMs Set up an intelligent MCP client that uses LLM to interpret natural commands and integrate everything with VS Code and GitHub Copilot. September 2, 6:00 PM Real-Time with SSE and HTTP Streaming Add real-time communication to your MCP server using Server-Sent Events and streamable HTTP. September 4, 6:00 PM Deploy MCP on Azure Add Real-Time Communication with Server-Sent Events to Your MCP Server and Professionally Deploy on Azure Container Apps. September 9, 6:00 PM 📅 Spanish edition - 9AM CST (Central Standard Time, Mexico City) ✅ Check the time in your location: 11am ET, 8am PT, 9am CST e 5pm CET - Register at MCP Bootcamp LATAM Session Title Date & Time (CST) Creando tu Primer Servidor MCP Construye desde cero un servidor MCP funcional en Python. Aprende los conceptos fundamentales del protocolo y prueba tu implementación usando herramientas oficiales. August 18, 09:00 AM Integración de MCP con LLMs Configura un cliente MCP inteligente que utilice LLM para interpretar comandos en lenguaje natural e intégralo con VS Code y GitHub Copilot. August 20, 09:00 AM MCP en Tiempo Real y Deploy en Azure Agrega comunicación en tiempo real con Server-Sent Events a tu servidor MCP y realiza un despliegue profesional en Azure Container Apps. August 25, 09:00 AM Comunicación en tiempo real con SSE y transmisión HTTP Agrega comunicación en tiempo real con Server-Sent Events a tu servidor MCP y realiza un despliegue profesional en Azure Container Apps. September 1, 09:00 AM 📅 Portuguese edition - 12PM BRT (Brasília Time) ✅ Register at MCP Bootcamp | Brasil Session Title Date & Time (BRT) Criando seu Primeiro MCP Server Construa do zero um servidor MCP funcional em Python. Aprenda os conceitos fundamentais do protocolo e teste sua implementação usando ferramentas oficiais. August 19, 12:00 PM Integração de MCP com LLMs Configure um cliente MCP inteligente que usa LLM para interpretar comandos naturais e integre tudo com VS Code e GitHub Copilot. August 21, 12:00 PM Deploy no Azure Adicione comunicação em tempo real com Server-Sent Events ao seu servidor MCP e faça deploy profissional na Azure Container Apps. August 26, 12:00 PM Comunicação em Tempo Real com SSE e HTTP Streaming Aprenda a adicionar comunicação em tempo real ao seu servidor MCP usando Server-Sent Events (SSE) e streaming HTTP. August 28, 12:00 PMSwagger Auto-Generation on MCP Server
Would you like to generate a swagger.json directly on an MCP server on-the-fly? In many use cases, using remote MCP servers is not uncommon. In particular, if you're using Azure API Management (APIM), Azure API Center (APIC) or Copilot Studio in Power Platform, integrating with remote MCP servers is inevitable.JS AI Build‑a‑thon: Wrapping Up an Epic June 2025!
After weeks of building, testing, and learning — we’re officially wrapping up the first-ever JS AI Build-a-thon 🎉. This wasn't your average coding challenge. This was a hands-on journey where JavaScript and TypeScript developers dove deep into real-world AI concepts — from local GenAI prototyping to building intelligent agents and deploying production-ready apps. Whether you joined from the start or hopped on midway, you built something that matters — and that’s worth celebrating. Replay the Journey No worries if you joined late or want to revisit any part of the journey. The JS AI Build-a-thon was designed to let you learn at your own pace, so whether you're starting now or polishing up your final project, here’s your complete quest map: Build-a-thon set up guide: https://aka.ms/JSAIBuildathonSetup Quest 1: 🔧 Build your first GenAI app locally with GitHub Models 👉🏽 https://aka.ms/JSAIBuildathonQuest1 Quest 2: ☁️ Move your AI prototype to Azure AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest Quest 3: 🎨 Add a chat UI using Vite + Lit 👉🏽 https://aka.ms/JSAIBuildathonQuest3 Quest 4: 📄 Enhance your app with RAG (Chat with Your Data) 👉🏽 https://aka.ms/JSAIBuildathonQuest4 Quest 5: 🧠 Add memory and context to your AI app 👉🏽 https://aka.ms/JSAIBuildathonQuest5 Quest 6: ⚙️ Build your first AI Agent using AI Foundry 👉🏽 https://aka.ms/JSAIBuildathonQuest6 Quest 7: 🧩 Equip your agent with tools from an MCP server 👉🏽 https://aka.ms/JSAIBuildathonQuest7 Quest 8: 💬 Ground your agent with real-time search using Bing 👉🏽 https://aka.ms/JSAIBuildathonQuest8 Quest 9: 🚀 Build a real-world AI project with full-stack templates 👉🏽 https://aka.ms/JSAIBuildathonQuest9 Link to our space in the AI Discord Community: https://aka.ms/JSAIonDiscord Project Submission Guidelines 📌 Quest 9 is where it all comes together. Participants chose a problem, picked a template, customized it, submitted it, and rallied their community for support! 🏅 Claim Your Badge! Whether you completed select quests or went all the way, we celebrate your learning. If you participated in the June 2025 JS AI Build-a-thon, make sure to Submit the Participation Form to receive your participation badge recognizing your commitment to upskilling in AI with JavaScript/ TypeScript. What’s Next? We’re not done. In fact, we’re just getting started. We’re already cooking up JS AI Build-a-thon v2, which will introduce: Running everything locally with Foundry Local Real-world RAG with vector databases Advanced agent patterns with remote MCPs And much more based on your feedback Want to shape what comes next? Drop your ideas in the participation form and in our Discord. In the meantime, add these resources to your JavaScript + AI Dev Pack: 🔗 Microsoft for JavaScript developers 📚 Generative AI for Beginners with JavaScript Wrap-Up This build-a-thon showed what’s possible when developers are empowered to learn by doing. You didn’t just follow tutorials — you shipped features, connected services, and created working AI experiences. We can’t wait to see what you build next. 👉 Bookmark the repo 👉 Join the community on Join the Azure AI Foundry Discord Server! 👉 Stay building Until next time — keep coding, keep shipping!
