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.Drive demand for your offers with solution area campaigns in a box.
Take your marketing campaigns further with campaigns in a box (CiaBs), collections of partner-ready, high-quality marketing assets designed to deepen customer engagement, simplify your marketing efforts, and grow revenue. Microsoft offers both new and refreshed campaigns for the following solution areas: Data & AI (Azure), Modern Work, Business Applications, Digital & App Innovation (Azure), Infrastructure (Azure), and Security. Check out the latest CiaBs below and get started today by visiting the Partner Marketing Center, where you’ll find resources such as step-by-step execution guides, customizable nurture tactics, and assets including presentations, e-books, infographics, and more. AI transformation Generate interest in AI adoption among your customers. As AI technology grabs headlines and captures imaginations, use this campaign to illustrate your audience’s unique opportunity to harness the power of AI to deliver value faster. Learn more about the campaign: AI Transformation (formerly Era of AI): Show audiences how to take advantage of the potential of AI to drive business value and showcase the usefulness of Microsoft AI solutions delivered and tailored by your organization. Data & AI (Azure) Our Data & AI campaigns demonstrate how your customers can win customers with AI-enabled differentiation. Show how they can transform their businesses with generative AI, a unified data estate, and solutions like Microsoft Fabric, Microsoft Power BI, and Azure Databricks. Campaigns include: Unify your intelligent data - Banking: Help your banking customers understand how you can help them break down data silos, meet compliance demands, and deliver on rising customer expectations. Innovate with the Azure AI platform: Help your customers understand the potential of generative AI solutions to differentiate themselves in the market—and inspire them to build GenAI solutions with the Azure AI platform. Unify your intelligent data and analytics platform - ENT: Show enterprise audiences how unifying data and analytics on an open foundation can help streamline data transformation and business intelligence. Unify your intelligent data and analytics platform - SMB: Create awareness and urgency for SMBs to adopt Microsoft Fabric as the AI-powered, unified data platform that will suit their analytics needs. Modern Work Our Modern Work campaigns help current and potential customers understand how they can effectively transform their businesses with AI capabilities. Campaigns include: Connect and empower your frontline workers: Empower your customers' frontline workers with smart, AI-enhanced workflows with solutions based on Microsoft Teams and Microsoft 365 Copilot. Use this campaign to show your customers how they can make their frontline workers feel more connected, leading to improved productivity and efficiency. 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Campaigns include: AI-powered customer service: Highlight how AI-powered solutions like Microsoft Dynamics 365 are transforming customer service with more personalized experiences, smarter teamwork, and improved efficiency. Migrate and modernize your ERP with Microsoft Dynamics 365: Position yourself as the right partner to modernize or replace your customers' legacy on-premises ERP systems with a Copilot-powered ERP. Business Central for SMB: Offer customers Microsoft Dynamics 365, a comprehensive business management solution that connects finance, sales, service, and operations teams with a single application to boost productivity and improve decision-making. AI-powered CRM: Help your customers enhance their customer experiences and close more deals with Microsoft 365 Dynamics Sales by making data AI-ready, which empowers them to create effective marketing content with Microsoft 365 Copilot and pass qualified leads on to sales teams. Use this campaign to show audiences how Copilot and AI can supercharge their CRM to increase productivity and efficiency, ultimately leading to better customer outcomes. Modernize at scale with AI and Microsoft Power Platform: This campaign is designed to introduce the business values unlocked with Microsoft Power Platform, show how low-code solutions can accelerate development and drive productivity, and position your company as a valuable asset in the deployment of these solutions. Digital & App Innovation (Azure) Position yourself as the strategic AI partner of choice and empower your customers to grow their businesses by helping them gain agility and build new AI applications faster with intelligent experiences. Campaigns include: Build and modernize AI apps: Help customers building new AI-infused applications and modernizing their application estate take advantage of the Azure AI application platform. 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Building a Multi-Agent System with Azure AI Agent Service: Campus Event Management
Personal Background My name is Peace Silly. I studied French and Spanish at the University of Oxford, where I developed a strong interest in how language is structured and interpreted. That curiosity about syntax and meaning eventually led me to computer science, which I came to see as another language built on logic and structure. In the academic year 2024–2025, I completed the MSc Computer Science at University College London, where I developed this project as part of my Master’s thesis. Project Introduction Can large-scale event management be handled through a simple chat interface? This was the question that guided my Master’s thesis project at UCL. As part of the Industry Exchange Network (IXN) and in collaboration with Microsoft, I set out to explore how conversational interfaces and autonomous AI agents could simplify one of the most underestimated coordination challenges in campus life: managing events across multiple departments, societies, and facilities. At large universities, event management is rarely straightforward. Rooms are shared between academic timetables, student societies, and one-off events. A single lecture theatre might host a departmental seminar in the morning, a society meeting in the afternoon, and a careers talk in the evening, each relying on different systems, staff, and communication chains. Double bookings, last-minute cancellations, and maintenance issues are common, and coordinating changes often means long email threads, manual spreadsheets, and frustrated users. These inefficiencies do more than waste time; they directly affect how a campus functions day to day. When venues are unavailable or notifications fail to reach the right people, even small scheduling errors can ripple across entire departments. A smarter, more adaptive approach was needed, one that could manage complex workflows autonomously while remaining intuitive and human for end users. The result was the Event Management Multi-Agent System, a cloud-based platform where staff and students can query events, book rooms, and reschedule activities simply by chatting. Behind the scenes, a network of Azure-powered AI agents collaborates to handle scheduling, communication, and maintenance in real time, working together to keep the campus running smoothly. The user scenario shown in the figure below exemplifies the vision that guided the development of this multi-agent system. Starting with Microsoft Learning Resources I began my journey with Microsoft’s tutorial Build Your First Agent with Azure AI Foundry which introduced the fundamentals of the Azure AI Agent Service and provided an ideal foundation for experimentation. Within a few weeks, using the Azure Foundry environment, I extended those foundations into a fully functional multi-agent system. Azure Foundry’s visual interface was an invaluable learning space. It allowed me to deploy, test, and adjust model parameters such as temperature, system prompts, and function calling while observing how each change influenced the agents’ reasoning and collaboration. Through these experiments, I developed a strong conceptual understanding of orchestration and coordination before moving to the command line for more complex development later. When development issues inevitably arose, I relied on the Discord support community and the GitHub forum for troubleshooting. These communities were instrumental in addressing configuration issues and providing practical examples, ensuring that each agent performed reliably within the shared-thread framework. This early engagement with Microsoft’s learning materials not only accelerated my technical progress but also shaped how I approached experimentation, debugging, and iteration. It transformed a steep learning curve into a structured, hands-on process that mirrored professional software development practice. A Decentralised Team of AI Agents The system’s intelligence is distributed across three specialised agents, powered by OpenAI’s GPT-4.1 models through Azure OpenAI Service. They each perform a distinct role within the event management workflow: Scheduling Agent – interprets natural language requests, checks room availability, and allocates suitable venues. Communications Agent – notifies stakeholders when events are booked, modified, or cancelled. Maintenance Agent – monitors room readiness, posts fault reports when venues become unavailable, and triggers rescheduling when needed. Each agent operates independently but communicates through a shared thread, a transparent message log that serves as the coordination backbone. This thread acts as a persistent state space where agents post updates, react to changes, and maintain a record of every decision. For example, when a maintenance fault is detected, the Maintenance Agent logs the issue, the Scheduling Agent identifies an alternative venue, and the Communications Agent automatically notifies attendees. These interactions happen autonomously, with each agent responding to the evolving context recorded in the shared thread. Interfaces and Backend The system was designed with both developer-focused and user-facing interfaces, supporting rapid iteration and intuitive interaction. The Terminal Interface Initially, the agents were deployed and tested through a terminal interface, which provided a controlled environment for debugging and verifying logic step by step. This setup allowed quick testing of individual agents and observation of their interactions within the shared thread. The Chat Interface As the project evolved, I introduced a lightweight chat interface to make the system accessible to staff and students. This interface allows users to book rooms, query events, and reschedule activities using plain language. Recognising that some users might still want to see what happens behind the scenes, I added an optional toggle that reveals the intermediate steps of agent reasoning. This transparency feature proved valuable for debugging and for more technical users who wanted to understand how the agents collaborated. When a user interacts with the chat interface, they are effectively communicating with the Scheduling Agent, which acts as the primary entry point. The Scheduling Agent interprets natural-language commands such as “Book the Engineering Auditorium for Friday at 2 PM” or “Reschedule the robotics demo to another room.” It then coordinates with the Maintenance and Communications Agents to complete the process. Behind the scenes, the chat interface connects to a FastAPI backend responsible for core logic and data access. A Flask + HTMX layer handles lightweight rendering and interactivity, while the Azure AI Agent Service manages orchestration and shared-thread coordination. This combination enables seamless agent communication and reliable task execution without exposing any of the underlying complexity to the end user. Automated Notifications and Fault Detection Once an event is scheduled, the Scheduling Agent posts the confirmation to the shared thread. The Communications Agent, which subscribes to thread updates, automatically sends notifications to all relevant stakeholders by email. This ensures that every participant stays informed without any manual follow-up. The Maintenance Agent runs routine availability checks. If a fault is detected, it logs the issue to the shared thread, prompting the Scheduling Agent to find an alternative room. The Communications Agent then notifies attendees of the change, ensuring minimal disruption to ongoing events. Testing and Evaluation The system underwent several layers of testing to validate both functional and non-functional requirements. Unit and Integration Tests Backend reliability was evaluated through unit and integration tests to ensure that room allocation, conflict detection, and database operations behaved as intended. Automated test scripts verified end-to-end workflows for event creation, modification, and cancellation across all agents. Integration results confirmed that the shared-thread orchestration functioned correctly, with all test cases passing consistently. However, coverage analysis revealed that approximately 60% of the codebase was tested, leaving some areas such as Azure service integration and error-handling paths outside automated validation. These trade-offs were deliberate, balancing test depth with project scope and the constraints of mocking live dependencies. Azure AI Evaluation While functional testing confirmed correctness, it did not capture the agents’ reasoning or language quality. To assess this, I used Azure AI Evaluation, which measures conversational performance across metrics such as relevance, coherence, fluency, and groundedness. The results showed high scores in relevance (4.33) and groundedness (4.67), confirming the agents’ ability to generate accurate and context-aware responses. However, slightly lower fluency scores and weaker performance in multi-turn tasks revealed a retrieval–execution gap typical in task-oriented dialogue systems. Limitations and Insights The evaluation also surfaced several key limitations: Synthetic data: All tests were conducted with simulated datasets rather than live campus systems, limiting generalisability. Scalability: A non-functional requirement in the form of horizontal scalability was not tested. The architecture supports scaling conceptually but requires validation under heavier load. Despite these constraints, the testing process confirmed that the system was both technically reliable and linguistically robust, capable of autonomous coordination under normal conditions. The results provided a realistic picture of what worked well and what future iterations should focus on improving. Impact and Future Work This project demonstrates how conversational AI and multi-agent orchestration can streamline real operational processes. By combining Azure AI Agent Services with modular design principles, the system automates scheduling, communication, and maintenance while keeping the user experience simple and intuitive. The architecture also establishes a foundation for future extensions: Predictive maintenance to anticipate venue faults before they occur. Microsoft Teams integration for seamless in-chat scheduling. Scalability testing and real-user trials to validate performance at institutional scale. Beyond its technical results, the project underscores the potential of multi-agent systems in real-world coordination tasks. It illustrates how modularity, transparency, and intelligent orchestration can make everyday workflows more efficient and human-centred. Acknowledgements What began with a simple Microsoft tutorial evolved into a working prototype that reimagines how campuses could manage their daily operations through conversation and collaboration. This was both a challenging and rewarding journey, and I am deeply grateful to Professor Graham Roberts (UCL) and Professor Lee Stott (Microsoft) for their guidance, feedback, and support throughout the project.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.Transform business process with agentic business applications (Americas)
September 30 - October 1, 2025 | 7:00 AM – 10:00 AM AMER (PDT) Overview This bootcamp is designed to equip you with the AI skills and clarity needed to harness the power of Copilot Studio and AI Agents in Dynamics 365. Participants will learn what AI agents are, why they matter in Dynamics 365, and how to design and build agents that address customer needs today while preparing for the AI-native ERP and CRM future. Building from the fundamentals of Copilot Studio and its integration across Dynamics 365 applications, we’ll explore how first-party agents are built, why Microsoft created them, and where their current limitations open opportunities for partner-led innovation. We’ll then expand into third-party agent design and extensibility, showing how partners can create differentiated solutions that deliver unique value. Finally, we will provide a forward-looking perspective on Microsoft’s strategy with Model Context Protocol (MCP), Agent-to-Agent (A2A) orchestration, and AI-native business applications - inspiring partners to create industry-specific agents that solve real customer pain points and showcase their expertise. Join this virtual event to accelerate your technical readiness journey on AI agents in Dynamics 365. Register today and mark your calendars to gain valuable insights from our Microsoft SMEs. Don’t miss this opportunity to learn about the latest developments and elevate your partnership with Microsoft Event prerequisites Participants should have some familiarity and work experience with the associated solutions. Additionally, we suggest having knowledge of the relevant role-based certification content (although passing the exam is not mandatory). You can find free self-paced learning content and technical documentation related to the workshop topics at Microsoft Learn. Earn a digital badge Attendees who participate in the live sessions of this workshop will earn a digital badge. These badges, which serve as a testament to your engagement and learning, can be conveniently accessed and shared through the Credly digital platform. Please note that accessing on-demand content does not meet the criteria for earning a badge. REGISTER TODAY!Build expertise, earn recognition, and drive transformation with Microsoft skilling
At Microsoft, we know that every innovation journey starts with the right skills. Our goal is to make your skilling experience streamlined and effective, so you spend less time searching for resources and more time innovating. We have designed the Partner Skilling Hub as a central portal where you can build new capabilities, accelerate organizational growth, and learn how to set your solutions apart and create meaningful customer outcomes. With advanced search and filters by category, solution area, and skill level, you can quickly find live events, self-paced courses, and training opportunities tailored to your needs. Below are just some of the skilling resources available to you through this new portal so you can spend less time searching and more time innovating. Continue reading hereJoin the Movement at Azure Dev Summit!
Azure Dev Summit is a chance for you to join a community of developers, architects, and tech leaders building with Azure, .NET, and Microsoft AI. This isn’t just another conference. It is a Microsoft-sponsored celebration of innovation, learning, and connection — and we’re bringing some of the most inspiring voices in tech to the stage. Find out more about the speakers at Azure Dev Summit and make sure to register! https://azuredevsummit.com/
