Edge AI for Student Developers: Learn to Run AI Locally
AI isn’t just for the cloud anymore. With the rise of Small Language Models (SLMs) and powerful local inference tools, developers can now run intelligent applications directly on laptops, phones, and edge devices—no internet required. If you're a student developer curious about building AI that works offline, privately, and fast, Microsoft’s Edge AI for Beginners course is your perfect starting point. What Is Edge AI? Edge AI refers to running AI models directly on local hardware—like your laptop, mobile device, or embedded system—without relying on cloud servers. This approach offers: ⚡ Real-time performance 🔒 Enhanced privacy (no data leaves your device) 🌐 Offline functionality 💸 Reduced cloud costs Whether you're building a chatbot that works without Wi-Fi or optimizing AI for low-power devices, Edge AI is the future of intelligent, responsive apps. About the Course Edge AI for Beginners is a free, open-source curriculum designed to help you: Understand the fundamentals of Edge AI and local inference Explore Small Language Models like Phi-2, Mistral-7B, and Gemma Deploy models using tools like Llama.cpp, Olive, MLX, and OpenVINO Build cross-platform apps that run AI locally on Windows, macOS, Linux, and mobile The course is hosted on GitHub and includes hands-on labs, quizzes, and real-world examples. You can fork it, remix it, and contribute to the community. What You’ll Learn Module Focus 01. Introduction What is Edge AI and why it matters 02. SLMs Overview of small language models 03. Deployment Running models locally with various tools 04. Optimization Speeding up inference and reducing memory 05. Applications Building real-world Edge AI apps Each module is beginner-friendly and includes practical exercises to help you build and deploy your own local AI solutions. Who Should Join? Student developers curious about AI beyond the cloud Hackathon participants looking to build offline-capable apps Makers and builders interested in privacy-first AI Anyone who wants to explore the future of on-device intelligence No prior AI experience required just a willingness to learn and experiment. Why It Matters Edge AI is a game-changer for developers. It enables smarter, faster, and more private applications that work anywhere. By learning how to deploy AI locally, you’ll gain skills that are increasingly in demand across industries—from healthcare to robotics to consumer tech. Plus, the course is: 💯 Free and open-source 🧠 Backed by Microsoft’s best practices 🧪 Hands-on and project-based 🌐 Continuously updated Ready to Start? Head to aka.ms/edgeai-for-beginners and dive into the modules. Whether you're coding in your dorm room or presenting at your next hackathon, this course will help you build smarter AI apps that run right where you need them on the edge.Foundry Fridays: Your Gateway to Azure AI Discovery
🎓 What Is Foundry Fridays? Every Friday at 1:30 PM ET, join the Azure AI Foundry Discord Community https://aka.ms/model-mondays/discord for a 30-minute live Ask Me Anything (AMA) session. It’s your chance to connect with the experts behind Azure AI—Principal PMs, researchers, and engineers—who are building the tools you’ll use in classrooms, hackathons, and real-world projects. Whether you're experimenting with model fine-tuning, curious about local inference, or diving into agentic workflows and open-source tooling, this is where your questions get answered live and unscripted. 💡 Why Students & Educators Should Join Direct Access to Experts Ask your questions live and get real-time insights from the people building Azure AI. Weekly Themes That Matter From model routing and MCP registries to SAMBA architectures, AI Agents, Model Router, Deployment Templates each week brings a new topic to explore. Community-Led Conversations Hosted by leaders like Nitya Narasimhan and Lee Stott, these sessions are interactive, inclusive, and designed to spotlight your questions. No Slides, Just Substance Skip the lectures—this is about real talk, real tech, and real learning. 📚 Bonus Learning: Model Mondays Want even more? Catch up on the Model Mondays series on demand at https://aka.ms/model-mondays and get ready for Season 3, streaming every Monday at 1:30 PM ET. 🚀 How to Join Join the Discord: https://aka.ms/model-mondays/discord Find the AMA: Check the #community-calls and #model-mondays channels or look for pinned events. Ask Anything: Bring your questions, ideas, or just listen in. No registration needed. 💬 Final Thoughts Whether you're coding your first AI project, mentoring students, or researching the next big thing listen and ask the experts questions and hear from the wider community. Foundry Fridays is your space to learn, connect, and grow. So grab your headphones, jump into Discord, and let’s shape the future of AI—together. 🗓️ Fridays | 1:30 PM ET 📍 Azure AI Foundry Discord 🔗 https://aka.ms/model-mondays/discordPreparing for Your Organization’s AI Workloads – Student Learning Pathways
This structured plan helps students: Plans | Microsoft Learn Build foundational knowledge of AI in the cloud. Learn how enterprise-level infrastructure supports responsible, scalable AI deployments. Explore governance and monitoring strategies to ensure security and compliance. And the best part? It’s built using Microsoft’s existing training resources plus some brand-new modules to give you an edge. Your AI Readiness Journey on Azure 🎯 Milestone 1: Getting Started with AI on Azure https://learn.microsoft.com/training/paths/introduction-to-ai-on-azure/ Begin with the basics—from machine learning concepts to practical uses of Azure AI services. 🛡️ Milestone 2: Infrastructure Essentials https://learn.microsoft.com/training/paths/manage-iam-for-ai-workloads-on-azure/ https://learn.microsoft.com/training/paths/manage-network-access-ai-workloads/ Learn how enterprises secure access and manage identities—critical for real-world applications. 📊 Milestone 3: Monitoring AI Services https://learn.microsoft.com/training/paths/monitor-ai-workloads-on-azure/ Discover how businesses ensure their models perform safely and consistently at scale. 🏛️ Milestone 4: Advanced Management & Governance https://learn.microsoft.com/training/paths/ai-workloads-governance/ Master how organizations prevent data leaks and enforce responsible AI usage. 🆕 New Training Content Just for You To make this roadmap even more student-friendly, Microsoft has introduced updated and brand-new modules, including: Azure ML Authentication & Authorization Secure Azure AI Services Restrict Workspace Network Traffic Monitor Azure ML Prevent Data Exfiltration Govern AI Services with Azure Policy 🔗 Ready to Dive In? Whether you're exploring a career in AI or just getting started with Azure, these learning paths will level up your skills while helping you understand how real-world teams manage complex AI workloads. Start your journey on Microsoft Learn and become the architect of tomorrow’s intelligent systems. 💡 Would you like a version formatted for your internal newsletter or maybe something more conversational for social media? I can easily tailor it to fit the tone or medium you're aiming for.Curious About Model Context Protocol? Dive into MCP with Us!
Global Workshops for All Skill Levels We’re hosting a series of free online workshops to introduce you to MCP—available in multiple languages and programming languages! You’ll get hands-on experience building your first MCP server, guided by friendly experts ready to answer your questions. Register now: https://aka.ms/letslearnmcp Who Should Join? This workshop is built for: Students exploring tech careers Beginner devs eager to learn how AI agents and MCP works Curious coders and seasoned pros alike If you’ve got some code curiosity and a laptop, you’re good to go. Workshop Schedule (English Sessions) Date Tech Focus Registration Link July 9 C# Join Here July 15 Java Join Here July 16 Python Join Here July 17 C# + Visual Studio Join Here July 21 TypeScript Join Here Multilingual Sessions We’re also hosting workshops in Spanish, Portuguese, Japanese, Korean, Chinese, Vietnamese, and more! Explore different tech stacks while learning in your preferred language: Date Language Technology Link July 15 한국어 (Korean) C# Join July 15 日本語 (Japanese) C# Join July 17 Español C# Join July 18 Tiếng Việt C# Join July 18 한국어 JavaScript Join July 22 한국어 Python Join July 22 Português Java Join July 23 中文 (Chinese) C# Join July 23 Türkçe C# Join July 23 Español JavaScript/TS Join July 23 Português C# Join July 24 Deutsch Java Join July 24 Italiano Python Join 🗓️ Save your seat: https://aka.ms/letslearnmcp What You’ll Need Before the event starts, make sure you’ve got: Visual Studio Code set up for your language of choice Docker installed A GitHub account (you can sign up for Copilot for free!) A curious mindset—no MCP experience required You can check out the MCP for Beginner course at https://aka.ms/mcp-for-beginners What’s Next? MCP Dev Days! Once you’ve wrapped up the workshop, why not go deeper? MCP Dev Days is happening July 29–30, and it’s packed with pro sessions from the Microsoft team and beyond. You’ll explore the MCP ecosystem, learn from insiders, and connect with other learners and devs. 👉 Info and registration: https://aka.ms/mcpdevdays Whether you're writing your first line of code or fine-tuning models like a pro, MCP is a game-changer. Come learn with us, and let’s build the future togetherMulti-Agent Systems and MCP Tools Integration with Azure AI Foundry
The Power of Connected Agents: Building Multi-Agent Systems Imagine trying to build an AI system that can handle complex workflows like managing support tickets, analyzing data from multiple sources, or providing comprehensive recommendations. Sounds challenging, right? That's where multi-agent systems come in! The Develop a multi-agent solution with Azure AI Foundry Agent Services module introduces you to the concept of connected agents a game changing approach that allows you to break down complex tasks into specialized roles handled by different AI agents. Why Connected Agents Matter As a student developer, you might wonder why you'd need multiple agents when a single agent can handle many tasks. Here's why this approach is transformative: 1. Simplified Complexity: Instead of building one massive agent that does everything (and becomes difficult to maintain), you can create smaller, specialized agents with clearly defined responsibilities. 2. No Custom Orchestration Required: The main agent naturally delegates tasks using natural language - no need to write complex routing logic or orchestration code. 3. Better Reliability and Debugging: When something goes wrong, it's much easier to identify which specific agent is causing issues rather than debugging a monolithic system. 4. Flexibility and Extensibility: Need to add a new capability? Just create a new connected agent without modifying your main agent or other parts of the system. How Multi-Agent Systems Work The architecture is surprisingly straightforward: 1. A main agent acts as the orchestrator, interpreting user requests and delegating tasks 2. Connected sub-agents perform specialized functions like data retrieval, analysis, or summarization 3. Results flow back to the main agent, which compiles the final response For example, imagine building a ticket triage system. When a new support ticket arrives, your main agent might: - Delegate to a classifier agent to determine the ticket type - Send the ticket to a priority-setting agent to determine urgency - Use a team-assignment agent to route it to the right department All this happens seamlessly without you having to write custom routing logic! Setting Up a Multi-Agent Solution The module walks you through the entire process: 1. Initializing the agents client 2. Creating connected agents with specialized roles 3. Registering them as tools for the main agent 4. Building the main agent that orchestrates the workflow 5. Running the complete system Taking It Further: Integrating MCP Tools with Azure AI Agents Once you've mastered multi-agent systems, the next level is connecting your agents to external tools and services. The Integrate MCP Tools with Azure AI Agents module teaches you how to use the Model Context Protocol (MCP) to give your agents access to a dynamic catalog of tools. What is Dynamic Tool Discovery? Traditionally, adding new tools to an AI agent meant hardcoding each one directly into your agent's code. But what if tools change frequently, or if different teams manage different tools? This approach quickly becomes unmanageable. Dynamic tool discovery through MCP solves this problem by: 1. Centralizing Tool Management: Tools are defined and managed in a central MCP server 2. Enabling Runtime Discovery: Agents discover available tools during runtime through the MCP client 3. Supporting Automatic Updates: When tools are updated on the server, agents automatically get the latest versions The MCP Server-Client Architecture The architecture involves two key components: 1. MCP Server: Acts as a registry for tools, hosting tool definitions decorated with `@mcp.tool`. Tools are exposed over HTTP when requested. 2. MCP Client: Acts as a bridge between your MCP server and Azure AI Agent. It discovers available tools, generates Python function stubs to wrap them, and registers those functions with your agent. This separation of concerns makes your AI solution more maintainable and adaptable to change. Setting Up MCP Integration The module guides you through the complete process: 1. Setting up an MCP server with tool definitions 2. Creating an MCP client to connect to the server 3. Dynamically discovering available tools 4. Wrapping tools in async functions for agent use 5. Registering the tools with your Azure AI agent Once set up, your agent can use any tool in the MCP catalog as if it were a native function, without any hardcoding required! Practical Applications for Student Developers As a student developer, how might you apply these concepts in real projects? Classroom Projects: - Build a research assistant that delegates to specialized agents for different academic subjects - Create a coding tutor that uses different agents for explaining concepts, debugging code, and suggesting improvements Hackathons: - Develop a sustainability app that uses connected agents to analyze environmental data from different sources - Create a personal finance advisor with specialized agents for budgeting, investment analysis, and financial planning Personal Portfolio Projects: - Build a content creation assistant with specialized agents for brainstorming, drafting, editing, and SEO optimization - Develop a health and wellness app that uses MCP tools to connect to fitness APIs, nutrition databases, and sleep tracking services Getting Started Ready to dive in? Both modules include hands-on exercises where you'll build real working examples: - A ticket triage system using connected agents - An inventory management assistant that integrates with MCP tools The prerequisites are straightforward: - Experience with deploying generative AI models in Azure AI Foundry - Programming experience with Python or C# Conclusion Multi-agent systems and MCP tools integration represent the next evolution in AI application development. By mastering these concepts, you'll be able to build more sophisticated, maintainable, and extensible AI solutions - skills that will make you stand out in internship applications and job interviews. The best part? These modules are designed with practical, hands-on learning in mind - perfect for student developers who learn by doing. So why not give them a try? Your future AI applications (and your resume) will thank you for it! Want to learn more about Model Context Protocol 'MCP' see MCP for Beginners Happy coding!Kickstart Your AI Development with the Model Context Protocol (MCP) Course
Model Context Protocol is an open standard that acts as a universal connector between AI models and the outside world. Think of MCP as “the USB-C of the AI world,” allowing AI systems to plug into APIs, databases, files, and other tools seamlessly. By adopting MCP, developers can create smarter, more useful AI applications that access up-to-date information and perform actions like a human developer would. To help developers learn this game-changing technology, Microsoft has created the “MCP for Beginners” course a free, open-source curriculum that guides you from the basics of MCP to building real-world AI integrations. Below, we’ll explore what MCP is, who this course is for, and how it empowers both beginners and intermediate developers to get started with MCP. What is MCP and Why Should Developers Care? Model Context Protocol (MCP) is a innovative framework designed to standardize interactions between AI models and client applications. In simpler terms, MCP is a communication bridge that lets your AI agent fetch live context from external sources (like APIs, documents, databases, or web services) and even take actions using tools. This means your AI apps are no longer limited to pre-trained knowledge they can dynamically retrieve data or execute commands, enabling far more powerful and context-aware behavior. Some key reasons MCP matters for developers: Seamless Integration of Tools & Data: MCP provides a unified way to connect AI to various data sources and tools, eliminating the need for ad-hoc, fragile integrations. Your AI agent can, for example, query a database or call a web API during a conversation all through a standardized protocol. Stay Up-to-Date: Because AI models can use MCP to access external information, they overcome the training data cutoff problem. They can fetch the latest facts, figures, or documents on demand, ensuring more accurate and timely responses. Industry Momentum: MCP is quickly gaining traction. Originally introduced by Microsoft and Anthropic in late 2024, it has since been adopted by major AI platforms (Replit, Sourcegraph, Hugging Face, and more) and spawned thousands of open-source connectors by early 2025. It’s an emerging standard – learning it now puts developers at the forefront of AI innovation. In short, MCP is transformative for AI development, and being proficient in it will help you build smarter AI solutions that can interact with the real world. The MCP for Beginners course is designed to make mastering this protocol accessible, with a structured learning path and hands-on examples. Introducing the MCP for Beginners Course “Model Context Protocol for Beginners” is an open-source, self-paced curriculum created by Microsoft to teach the concepts and fundamentals of MCP. Whether you’re completely new to MCP or have some experience, this course offers a comprehensive guide from the ground up. Key Features and Highlights: Structured Learning Path: The curriculum is organized as a multi-part guide (9 modules in total) that gradually builds your knowledge. It starts with the basics of MCP – What is MCP? Why does standardization matter? What are the use cases? – and then moves through core concepts, security considerations, getting started with coding, all the way to advanced topics and real-world case studies. This progression ensures you understand the “why” and “how” of MCP before tackling complex scenarios. Hands-On Coding Examples: This isn’t just theory – practical coding examples are a cornerstone of the course. You’ll find live code samples and mini-projects in multiple languages (C#, Java, JavaScript/TypeScript, and Python) for each concept. For instance, you’ll build a simple MCP-powered Calculator application as a project, exploring how to implement MCP clients and servers in your preferred language. By coding along, you cement your understanding and see MCP in action. Real-World Use Cases: The curriculum illustrates how MCP applies to real scenarios. It discusses practical use cases of MCP in AI pipelines (e.g. an AI agent pulling in documentation or database info on the fly) and includes case studies of early adopters. These examples help you connect what you learn to actual applications and solutions you might develop in your job. Broad Language Support: A unique aspect of this course is its multi-language approach – both in terms of programming and human languages. The content provides code implementations in several popular programming languages (so you can learn MCP in the context of C#, Java, Python, JavaScript, or TypeScript, as you prefer). In addition, the learning materials themselves are available in multiple human languages (English, plus translations like French, Spanish, German, Chinese, Japanese, Korean, Polish, etc.) to support learners worldwide. This inclusivity ensures that more developers can comfortably engage with the material. Up-to-Date and Open-Source: Being hosted on GitHub under MIT License, the curriculum is completely free to use and open for contributions. It’s maintained with the latest updates for example, automated workflows keep translations in sync so all language versions stay current. As MCP evolves, the course content can evolve with it. You can even join the community to suggest improvements or add content, making this a living learning resource. Official Resources & Community Support: The course links to official MCP documentation and specs for deeper reference, and it encourages learners to join thehttps;//aka.ms/ai/discord to discuss and get help. You won’t be learning alone; you can network with experts and peers, ask questions, and share progress. Microsoft’s open-source approach means you’re part of a community of practitioners from day one. Course Outline: (Modules at a Glance) Introduction to MCP: Overview of MCP, why standardization matters in AI, and the key benefits and use cases of using MCP. (Start here to understand the big picture.) Core Concepts: Deep dive into MCP’s architecture – understanding the client-server model, how requests and responses work, and the message schema. Learn the fundamental components that make up the protocol. Security in MCP: Identify potential security threats when building MCP-based systems and learn best practices to secure your AI integrations. Important for anyone planning to deploy MCP in production environments. Getting Started (Hands-On): Set up your environment and create your first MCP server and client. This module walks through basic implementation steps and shows how to integrate MCP with existing applications, so you get a service up and running that an AI agent can communicate with. MCP Calculator Project: A guided project where you build a simple MCP-powered application (a calculator) in the language of your choice. This hands-on exercise reinforces the concepts by implementing a real tool – you’ll see how an AI agent can use MCP to perform calculations via an external tool. Practical Implementation: Tips and techniques for using MCP SDKs across different languages. Covers debugging, testing, validation of MCP integrations, and how to design effective prompt workflows that leverage MCP’s capabilities. Advanced Topics: Going beyond the basics – explore multi-modal AI workflows (using MCP to handle not just text but other data types), scalability and performance tuning for MCP servers, and how MCP fits into larger enterprise architectures. This is where intermediate users can really deepen their expertise. Community Contributions: Learn how to contribute to the MCP ecosystem and the curriculum itself. This section shows you how to collaborate via GitHub, follow the project’s guidelines, and even extend the protocol with your own ideas. It underlines that MCP is a growing, community-driven standard. Insights from Early Adoption: Hear lessons learned from real-world MCP implementations. What challenges did early adopters face? What patterns and solutions worked best? Understanding these will prepare you to avoid pitfalls in your own projects. Best Practices and Case Studies: A roundup of do’s and don’ts when using MCP. This includes performance optimization techniques, designing fault-tolerant systems, and testing strategies. Plus, detailed case studies that walk through actual MCP solution architectures with diagrams and integration tips bringing everything you learned together in concrete examples. Who Should Take This Course? The MCP for Beginners course is geared towards developers if you build or work on AI-driven applications, this course is for you. The content specifically welcomes: Beginners in AI Integration: You might be a developer who's comfortable with languages like Python, C#, or Java but new to AI/LLMs or to MCP itself. This course will take you from zero knowledge of MCP to a level where you can build and deploy your own MCP-enabled services. You do not need prior experience with MCP or machine learning pipelines the introduction module will bring you up to speed on key concepts. (Basic programming skills and understanding of client-server or API concepts are the only prerequisites.) Intermediate Developers & AI Practitioners: If you have some experience building bots or AI features and want to enhance them with real-time data access, you’ll benefit greatly. The course’s later modules on advanced topics, security, and best practices are especially valuable for those looking to integrate MCP into existing projects or optimize their approach. Even if you've dabbled in MCP or a similar concept before, this curriculum will fill gaps in knowledge and provide structured insights that are hard to get from scattered documentation. AI Enthusiasts & Architects: Perhaps you’re an AI architect or tech lead exploring new frameworks for intelligent agents. This course serves as a comprehensive resource to evaluate MCP for your architecture. By walking through it, you’ll understand how MCP can fit into enterprise systems, what benefits it brings, and how to implement it in a maintainable way. It’s perfect for getting a broad yet detailed view of MCP’s capabilities before adopting it within a team. In essence, anyone interested in making AI applications more connected and powerful will find value here. From a solo hackathon coder to a professional solution architect, the material scales to your need. The course starts with fundamentals in an easy-to-grasp manner and then deepens into complex topics appealing to a wide range of skill levels. Prerequisites: The official prerequisites for the course are minimal: you should have basic knowledge of at least one programming language (C#, Java, or Python is recommended) and a general understanding of how client-server applications or APIs work. Familiarity with machine learning concepts is optional but can help. In short, if you can write simple programs and understand making API calls, you have everything you need to start learning MCP. Conclusion: Empower Your AI Projects with MCP The Model Context Protocol for Beginners course is more than just a tutorial – it’s a comprehensive journey that empowers you to build the next generation of AI applications. By demystifying MCP and equipping you with hands-on experience, this curriculum turns a seemingly complex concept into practical skills you can apply immediately. With MCP, you unlock capabilities like giving your AI agents real-time information access and the ability to use tools autonomously. That means as a developer, you can create solutions that are significantly more intelligent and useful. A chatbot that can search documents, a coding assistant that can consult APIs or run code, an AI service that seamlessly integrates with your database – all these become achievable when you know MCP. And thanks to this beginners-friendly course, you’ll be able to implement such features with confidence. Whether you are starting out in the AI development world or looking to sharpen your cutting-edge skills, the MCP for Beginners course has something for you. It condenses best practices, real-world lessons, and robust techniques into an accessible format. Learning MCP now will put you ahead of the curve, as this protocol rapidly becomes a cornerstone of AI integrations across the industry. So, are you ready to level up your AI development skills? Dive into the https://aka.ms/mcp-for-beginnerscourse and start building AI agents that can truly interact with the world around them. With the knowledge and experience gained, you’ll be prepared to create smarter, context-aware applications and be a part of the community driving AI innovation forward.Build your code-first agent with Azure AI Foundry: Self-Guided Workshop
Build your first Agent App Agentic AI is changing how we build intelligent apps - enabling software to reason, plan, and act for us. Learning to build AI agents is quickly becoming a must-have skill for anyone working with AI. Self-Guided Workshop Try our self-guided “Build your code-first agent with Azure AI Foundry” workshop to get hands-on with Azure AI Agent Service. You’ll learn to build, deploy, and interact with agents using Azure’s powerful tools. What is Azure AI Agent Service? Azure AI Agent Service lets you create, orchestrate, and manage AI-powered agents that can handle complex tasks, integrate with tools, and deploy securely. What Will You Learn? The basics of agentic AI apps and how they differ from traditional apps How to set up your Azure environment How to build your first agent How to test and interact with your agent Advanced features like tool integration and memory management Who Is This For? Anyone interested in building intelligent, goal-oriented agents — developers, data scientists, students, and AI enthusiasts. No prior experience with Azure AI Agent Service required. How Does the Workshop Work? Tip: Select the self-guided tab in Getting Started for the right instructions. Step-by-step guides at your own pace Code samples and templates Real-world scenarios Get Started See what agentic AI can do for you with the self-guided “Build your code-first agent with Azure AI Foundry” workshop. Build practical skills in one of AI’s most exciting areas. Try the workshop and start building agents that make a difference! Additional Resources Azure AI Foundry Documentation Azure AI Agent Service Overview Questions or feedback Questions or feedback? Visit the issues page. Happy learning and building with Azure AI Agent Service!Automating PowerPoint Generation with AI: A Learn Live Series Case Study
Introduction A Learn Live is a series of events where over a period of 45 to 60 minutes, a presenter walks attendees through a learning module or pathway. The show/series, takes you through a Microsoft Learn Module, Challenge or a particular sample. Between April 15 to May 13, we will be hosting a Learn Live series on "Master the Skills to Create AI Agents." This premise is necessary for the blog because I was tasked with generating slides for the different presenters. Challenge: generation of the slides The series is based on the learning path: Develop AI agents on Azure and each session tackles one of the learn modules in the path. In addition, Learn Live series usually have a presentation template each speaker is provided with to help run their sessions. Each session has the same format as the learn modules: an introduction, lesson content, an exercise (demo), knowledge check and summary of the module. As the content is already there and the presentation template is provided, it felt repetitive to do create the slides one by one. And that's where AI comes in - automating slide generation for Learn Live modules. Step 1 - Gathering modules data The first step was ensuring I had the data for the learn modules, which involved collecting all the necessary information from the learning path and organizing it in a way that can be easily processed by AI. The learn modules repo is private and I have access to the repo, but I wanted to build a solution that can be used externally as well. So instead of getting the data from the repository, I decided to scrape the learn modules using BeautifulSoup into a word document. I created a python script to extract the data, and it works as follows: Retrieving the HTML – It sends HTTP requests to the start page and each unit page. Parsing Content – Using BeautifulSoup, it extracts elements (headings, paragraphs, lists, etc.) from the page’s main content. Populating a Document – With python-docx, it creates and formats a Word document, adding the scraped content. Handling Duplicates – It ensures unique unit page links by removing duplicates. Polite Scraping – A short delay (using time.sleep) is added between requests to avoid overloading the server. First, I installed the necessary libraries using: pip install requests beautifulsoup4 python-docx. Next, I ran the script below, converting the units of the learn modules to a word document: import requests from bs4 import BeautifulSoup from docx import Document from urllib.parse import urljoin import time headers = {"User-Agent": "Mozilla/5.0"} base_url = "https://learn.microsoft.com/en-us/training/modules/orchestrate-semantic-kernel-multi-agent-solution/" def get_soup(url): response = requests.get(url, headers=headers) return BeautifulSoup(response.content, "html.parser") def extract_module_unit_links(start_url): soup = get_soup(start_url) nav_section = soup.find("ul", {"id": "unit-list"}) if not nav_section: print("❌ Could not find unit navigation.") return [] links = [] for a in nav_section.find_all("a", href=True): href = a["href"] full_url = urljoin(base_url, href) links.append(full_url) return list(dict.fromkeys(links)) # remove duplicates while preserving order def extract_content(soup, doc): main_content = soup.find("main") if not main_content: return for tag in main_content.find_all(["h1", "h2", "h3", "p", "li", "pre", "code"]): text = tag.get_text().strip() if not text: continue if tag.name == "h1": doc.add_heading(text, level=1) elif tag.name == "h2": doc.add_heading(text, level=2) elif tag.name == "h3": doc.add_heading(text, level=3) elif tag.name == "p": doc.add_paragraph(text) elif tag.name == "li": doc.add_paragraph(f"• {text}", style='ListBullet') elif tag.name in ["pre", "code"]: doc.add_paragraph(text, style='Intense Quote') def scrape_full_module(start_url, output_filename="Learn_Module.docx"): doc = Document() # Scrape and add the content from the start page print(f"📄 Scraping start page: {start_url}") start_soup = get_soup(start_url) extract_content(start_soup, doc) all_unit_links = extract_module_unit_links(start_url) if not all_unit_links: print("❌ No unit links found. Exiting.") return print(f"🔗 Found {len(all_unit_links)} unit pages.") for i, url in enumerate(all_unit_links, start=1): print(f"📄 Scraping page {i}: {url}") soup = get_soup(url) extract_content(soup, doc) time.sleep(1) # polite delay doc.save(output_filename) print(f"\n✅ Saved module to: {output_filename}") # 🟡 Replace this with any Learn module start page start_page = "https://learn.microsoft.com/en-us/training/modules/orchestrate-semantic-kernel-multi-agent-solution/" scrape_full_module(start_page, "Orchestrate with SK.docx") Step 2 - Utilizing Microsoft Copilot in PowerPoint To automate the slide generation, I used Microsoft Copilot in PowerPoint. This tool leverages AI to create slides based on the provided data. It simplifies the process and ensures consistency across all presentations. As I already had the slide template, I created a new presentation based on the template. Next, I used copilot in PowerPoint to generate the slides based on the presentation. How did I achieve this? I uploaded the word document generated from the learn modules to OneDrive In PowerPoint, I went over to Copilot and selected ```view prompts```, and selected the prompt: create presentations Next, I added the prompt below and the word document to generate the slides from the file. Create a set of slides based on the content of the document titled "Orchestrate with SK". The slides should cover the following sections: • Introduction • Understand the Semantic Kernel Agent Framework • Design an agent selection strategy • Define a chat termination strategy • Exercise - Develop a multi-agent solution • Knowledge check • Summary Slide Layout: Use the custom color scheme and layout provided in the template. Use Segoe UI family fonts for text and Consolas for code. Include visual elements such as images, charts, and abstract shapes where appropriate. Highlight key points and takeaways. Step 3 - Evaluating and Finalizing Slides Once the slides are generated, if you are happy with how they look, select keep it. The slides were generated based on the sessions I selected and had all the information needed. The next step was to evaluate the generated slides, add the Learn Live introduction, knowledge check and conclusion. The goal is to create high-quality presentations that effectively convey the learning content. What more can you do with Copilot in PowerPoint? Add speaker notes to the slides Use agents within PowerPoint to streamline your workflow. Create your own custom prompts for future use cases Summary - AI for automation In summary, using AI for slide generation can significantly streamline the process and save time. I was able to automate my work and only come in as a reviewer. The script and PowerPoint generation all took about 10 minutes, something that would have previously taken me an hour and I only needed to counter review based on the learn modules. It allowed for the creation of consistent and high-quality presentations, making it easier for presenters to focus on delivering the content. Now, my question to you is, how can you use AI in your day to day and automate any repetitive tasks?
