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.Practical ways to use AI in your Data Science and ML journey
Are you ready to take your learning from “just reading” to “actively doing”? We are launching a series of interactive events designed to help you bridge the gap between theory and practice, connect with peers, and learn directly from experts, all in a vibrant, supportive Discord community. The series will guide you on practical ways to use AI in your learning journey with Data Science and Machine Learning. Join the series on Discord at: https://aka.ms/ds4beginners/discord Why You Should Join Guided Pathways: Move seamlessly from the popular Data Science for Beginners and Machine Learning for Beginners curriculums to interact with fellow learners in the Azure AI Foundry Discord community. We’ve designed study plans, office hours sessions, and showcases so you can learn, ask questions, and get feedback in real time. Practical AI Skills: See how Artificial Intelligence can supercharge your learning. . Community & Collaboration: Find peer learners with similar goals, collaborate on projects, and get support from experienced moderators and MVPs. Machine Learning AMA 1 Theme: Learning Machine Learning with AI, responsibly. Date: Tuesday, 23 September (10AM PST | 1PM ET | 5PM GMT | 8PM EAT) What to Expect: Curriculum overview, hands-on AI demos, critique and correct a simple classifier, and peer collaboration. Link: https://aka.ms/learnwithai/ml Data Science AMA 2 Theme: Learning Data Science with AI, responsibly. Date: Tuesday, 30 September (10AM PST | 1PM ET | 5PM GMT | 8PM EAT) What to Expect: Curriculum overview, integrating AI into your learning, live Q&A, and practical tips for responsible AI use. Link: https://aka.ms/learnwithai/ds Showcase 1: GitHub Copilot for Data Science Theme: Hands-on AI Agents for Data Scientists Date: Thursday, 25 September (10AM PST | 1PM ET | 5PM GMT | 8PM EAT) What to Expect: Learn to use Copilot’s advanced features in Python and Jupyter Notebooks, customize agents, and extend your analyses. Link: https://aka.ms/learnwithai/agents How to Get Involved Join the Discord Channels: https://aka.ms/ds4beginners/discord https://aka.ms/ml4beginners/discord Prepare: Make sure you have a GitHub account, VS Code, Data Wrangler, ready for hands-on sessions. What Makes This Program Different? Discord first learning: Experience higher engagement and deeper connections. Curriculum-anchored activities: Every event is designed to build on what you’ve learned in the open-source curriculums. AI-powered study plans and resources: Get starter notebooks, prompt cards, and recap templates to accelerate your progress. Resources Hands-on workshop: https://aka.ms/ds-agents Data Science for beginner's curriculum: https://aka.ms/ds4beginners Machine Learning for beginner's curriculum: https://aka.ms/ml4beginners Ready to transform your learning journey? Join us for these exciting events and join us in the Azure AI Foundry Discord community. Whether you’re just starting out or looking to deepen your skills, there’s a place for you here!Announcing MSGraph Provider Public Preview and the Microsoft Terraform VSCode Extension
We are thrilled to announce two exciting developments in the Microsoft ecosystem for Terraform infrastructure-as-code (IaC) practitioners: the public preview of the Terraform Microsoft Graph (MSGraph) provider and the release of the Microsoft Terraform Visual Studio Code (VSCode) extension. These innovations are designed to streamline your workflow, empower your automation, and make managing Microsoft cloud resources easier than ever. Public Preview: Terraform Microsoft Graph (MSGraph) Provider The Terraform MSGraph provider empowers you to manage Entra APIs like privileged identity management as well as M365 Graph APIs like SharePoint sites from day 0 by leveraging the power and flexibility of HashiCorp Configuration Language (HCL) in Terraform. resource "msgraph_resource" "application" { url = "applications" body = { displayName = "My Application" } response_export_values = { all = "@" app_id = "appId" } } output "app_id" { value = msgraph_resource.application.output.app_id } output "all" { // it will output the whole response value = msgraph_resource.application.output.all } Historically, Terraform users could utilize the `azuread` provider to manage Entra features like users, groups, service principals, and applications. The new `msgraph` provider also supports these features and extends functionality to all beta and v1 Microsoft Graph endpoints. Querying role assignments for a service principal The below example shows how to use the `msgraph` provider to grant app permissions to a service principal: locals { MicrosoftGraphAppId = "00000003-0000-0000-c000-000000000000" # AppRoleAssignment userReadAllAppRoleId = one([for role in data.msgraph_resource.servicePrincipal_msgraph.output.all.value[0].appRoles : role.id if role.value == "User.Read.All"]) userReadWriteRoleId = one([for role in data.msgraph_resource.servicePrincipal_msgraph.output.all.value[0].oauth2PermissionScopes : role.id if role.value == "User.ReadWrite"]) # ServicePrincipal MSGraphServicePrincipalId = data.msgraph_resource.servicePrincipal_msgraph.output.all.value[0].id TestApplicationServicePrincipalId = msgraph_resource.servicePrincipal_application.output.all.id } data "msgraph_resource" "servicePrincipal_msgraph" { url = "servicePrincipals" query_parameters = { "$filter" = ["appId eq '${local.MicrosoftGraphAppId}'"] } response_export_values = { all = "@" } } resource "msgraph_resource" "application" { url = "applications" body = { displayName = "My Application" requiredResourceAccess = [ { resourceAppId = local.MicrosoftGraphAppId resourceAccess = [ { id = local.userReadAllAppRoleId type = "Scope" }, { id = local.userReadWriteRoleId type = "Scope" } ] } ] } response_export_values = { appId = "appId" } } resource "msgraph_resource" "servicePrincipal_application" { url = "servicePrincipals" body = { appId = msgraph_resource.application.output.appId } response_export_values = { all = "@" } } resource "msgraph_resource" "appRoleAssignment" { url = "servicePrincipals/${local.MSGraphServicePrincipalId}/appRoleAssignments" body = { appRoleId = local.userReadAllAppRoleId principalId = local.TestApplicationServicePrincipalId resourceId = local.MSGraphServicePrincipalId } } SharePoint & Outlook Notifications With your service principals properly configured, you can set up M365 endpoint workflows such an outlook notification template list as shown below. The actual service principal setup has been omitted from this code sample for the sake of brevity, but you will need Sites.Manage.All, Sites.ReadWrite.All, User.Read, and User.Read.All permissions for this example to work: data "msgraph_resource" "sharepoint_site_by_path" { url = "sites/microsoft.sharepoint.com:/sites/msgraphtest:" response_export_values = { full_response = "@" site_id = "id || ''" } } resource "msgraph_resource" "notification_templates_list" { url = "sites/${msgraph_resource.sharepoint_site_by_path.output.site_id}/lists" body = { displayName = "DevOps Notification Templates" description = "Centrally managed email templates for DevOps automation" template = "genericList" columns = [ { name = "TemplateName" text = { allowMultipleLines = false appendChangesToExistingText = false linesForEditing = 1 maxLength = 255 } }, { name = "Subject" text = { allowMultipleLines = false appendChangesToExistingText = false linesForEditing = 1 maxLength = 500 } }, { name = "HtmlBody" text = { allowMultipleLines = true appendChangesToExistingText = false linesForEditing = 10 maxLength = 10000 } }, { name = "Recipients" text = { allowMultipleLines = true appendChangesToExistingText = false linesForEditing = 3 maxLength = 1000 } }, { name = "TriggerConditions" text = { allowMultipleLines = true appendChangesToExistingText = false linesForEditing = 5 maxLength = 2000 } } ] } response_export_values = { list_id = "id" list_name = "displayName" web_url = "webUrl" } } The MSGraph provider is to AzureAD as the AzAPI provider is to AzureRM. Since support for resource types is automatic, you can access the latest features and functionality as soon as they're released via the provider. AzureAD will continue to serve as the convenience layer implementation of a subset of Entra APIs. We invite you to try the new provider today: - Deploy your first msgraph resources - Check out the registry page - Visit the provider GitHub Introducing the Microsoft Terraform VSCode Extension The new official Microsoft Terraform extension for Visual Studio Code consolidates AzureRM, AzAPI, and MSGraph VSCode support into a single powerful extension. The extension supports exporting Azure resources as Terraform code, as well as IntelliSense, syntax highlighting, and code sample generation. It replaces the Azure Terraform and AzAPI VSCode extensions and adds some new features. Installation & Migration New users can install the extension by searching “Microsoft Terraform” within Visual Studio Marketplace or their “Extensions” tab. Users can also click this link to the Visual Studio marketplace. Users of the “Azure Terraform” extension can navigate to “Extensions” tab and selecting the old extension. Select the “Migrate” button to move to the new extension. Users of the “Terraform AzAPI Provider” extension will be directed to the new extension: New Features Export Azure Resources As Terraform This feature allows you to export existing Azure resources as Terraform configuration blocks using Azure Export for Terraform. This helps you migrate existing Azure resources to Terraform-managed infrastructure. Open the Command Palette (Command+Shift+P on macOS and Ctrl+Shift+P on Windows/Linux). Search for and select the command Microsoft Terraform: Export Azure Resource as Terraform. Follow the prompts to select the Azure subscription and resource group containing the resources you want to export. Select the azurerm provider or the azapi provider to export the resources. The extension will generate the Terraform configuration blocks for the selected resources and display them in a new editor tab. Support for MSGraph The new extension comes fully equipped with intellisense, code completion, and code samples just like the AzAPI provider. See the next section for recorded examples of these features within the AzureRM & AzAPI providers. Preexisting Features Intelligent Code Completion: Benefit from context-aware suggestions, like property names or resource types. Code Samples: Quickly insert code samples for your resources: Paste as AzAPI: Copy your existing resource JSON or ARM Templates into VSCode with the Microsoft Terraform extension, and it will automatically convert your code into AzAPI. The below example takes a resource JSON from the Azure Portal and pastes it into VSCode as AzAPI: Migrate AzureRM to AzAPI: Move existing AzureRM code to the AzAPI provider whenever you wish to. Read more in the Guide to migrate AzureRM resources to AzAPI Feedback We value your feedback! You can share your experience with the Microsoft Terraform extension by running the command Microsoft Terraform: Show Survey from the Command Palette. Your input helps us improve the extension and better serve your needs. Conclusion Whether you are managing traditional Azure resources, modern Microsoft Graph environments, or a combination of both, the new MSGraph provider and Microsoft Terraform VS Code extension are designed to help you deliver robust, reliable infrastructure—faster and with greater confidence. Stay tuned for further updates, workshops, and community events as we continue to evolve these offerings. Your feedback and participation are invaluable as we build the next generation of infrastructure automation together.Quest 1 – I Want to Build a Local Gen AI Prototype
In this quest, you’ll build a local Gen AI app prototype using JavaScript or TypeScript. You’ll explore open-source models via GitHub, test them in a visual playground, and use them in real code — all from the comfort of VS Code with the AI Toolkit. It’s fast, hands-on, and sets you up to build real AI apps, starting with a sketch.JS AI Build-a-thon Setup in 5 Easy Steps
🔥 TL;DR — You’re 5 Steps Away from an AI Adventure Set up your project repo, follow the quests, build cool stuff, and level up. Everything’s automated, community-backed, and designed to help you actually learn AI — using the skills you already have. Let’s build the future. One quest at a time. 👉 Join the Build-a-thon | Chat on Discord🚨Introducing the JS AI Build-a-thon 🚨
We’re entering a future where AI-first and agentic developer experiences will shape how we build — and you don’t want to be left behind. This isn’t your average hackathon. It’s a hands-on, quest-driven learning experience designed for developers, packed with: Interactive quests that guide you step by step — from your first prototype to production-ready apps Community-powered support via our dedicated Discord and local, community-led study jams Showcase moments to share your journey, get inspired, and celebrate what you build Whether you're just starting your AI journey or looking to sharpen your skills with frameworks like LangChain.js, tools like the Azure AI Foundry and AI Toolkit Extensions, or diving deeper into agentic app design — this is your moment to start building.How to use Comments as Prompts in GitHub Copilot for Visual Studio
GitHub Copilot is a coding assistant powered by Artificial Intelligence (AI), which can run in various environments and help you be more efficient in your daily coding tasks. In this new short video, Bruno shows you how to use inline comments to generate code with GitHub Copilot.European AI and Cloud Summit 2025
Dusseldorf , Germany added 3,000+ tech enthusiast and hosted the Microsoft for Startups Cloud AI Pitch Competition between May 26-28, 2025. We were pleased to attend the European AI Cloud and Collaboration Summits and Biz Apps Summit– to participate and to gain so much back from everyone. It was a packed week filled with insights, feedback, and fun. Below is a recap of various aspects of the event - across keynotes, general sessions, breakout sessions, and the Expo Hall. The event in a nutshell: 3,000+ attendees 237 speakers overall – 98 Microsoft Valued Professionals, 16 Microsoft Valued Professional Regional Directorss, 51 from Microsoft Product Groups and Engineering 306 sessions | 13 tutorials (workshops) 70 sponsors | One giant Expo Hall PreDay Workshop AI Beginner Development Powerclass This workshop is designed to give you a hands-on introduction to the core concepts and best practices for interacting with OpenAI models in Azure AI Foundry portal. Innovate with Azure OpenAI's GPT-4o multimodal model in this hands-on experience in Azure AI Foundry. Learn the core concepts and best practices to effectively generate with text, sound, and images using GPT-4o-mini, DALL-E and GPT-4o-realtime. Create AI assistants that enhance user experiences and drive innovation. Workshop for you azure-ai-foundry/ai-tutorials: This repo includes a collection of tutorials to help you get started with building Generative AI applications using Azure AI Foundry. Each tutorial is designed to be self-contained and provides step-by-step instructions to guide you in the development process. Keynotes & general sessions Day 1 Keynote The Future of AI Is Already Here, Marco Casalaina, VP Products of Azure AI and AI Futurist at Microsoft This session discussed the new and revolutionary changes that you're about to see in AI - and how many of them are available for you to try now. Marco shared how AI is becoming ubiquitous, multimodal, multilingual, and autonomous, and how it will change our lives and our businesses. This session covered: • Incredible advances in multilingual AI • How Copilot (and every AI) are grounded to data, and how we do it in Azure OpenAI • Responsible AI, including evaluation for correctness, and real time content safety • The rise of AI Agents • And how AI is going to move from question-answering to taking action Day 2 Keynote Leveraging Microsoft AI: Navigating the EU AI Act and Unlocking Future Opportunities, Azar Koulibaly, General Manager and Associate General Counsel This session was for developers and business decision makers, Azar set the stage for Microsoft’s advancements in AI and how they align with the latest regulatory framework. Exploring the EU AI Act, its key components, and its implications for AI development and deployment within the European Union. The audience gained a comprehensive understanding of the EU AI Act's objectives, including the promotion of trustworthy AI, the mitigation of risks, and the enhancement of transparency and accountability. Learning aboutMicrosoft's Cloud and AI Services provide robust support for compliance with these new regulations, ensuring that your AI projects are both innovative and legally sound and Microsoft trust center resources. He delved into the opportunities that come with using Microsoft’s state-of-the-art tools, services, and technologies. Discover how partnering with Microsoft can accelerate your AI initiatives, drive business growth, and create competitive advantages in an evolving regulatory landscape. Join us to unlock the full potential of AI while navigating the complexities of the EU AI Act with confidence. General Sessions It is crucial to ensure your organization is technically ready for the full potential of AI. The sessions focused on technical readiness and ensuring you have the latest guidance. Our experts will shared the best practices and provide guidance on how to leverage AI and Azure AI Foundry to maximize the benefits of Agents, LLM and Generative within your organization. Expo Hall + Cloud AI Statup Stage + tutorials The Expo Hall was Buzzing with demos, discussions, interviews, podcasts, lightning talks, popcorn, catering trucks, cotton candy, SWAG, prizes, and community. There was a busy Cloud AI StartupStage, a Business Stage, and shorter talks delivered in front of a shiny airstream trailer. Cloud AI Startup Stage This was a highly informative and engaging event focused on Artificial Intelligence (AI) and its potential for startups. The Microsoft for Startups is a platform to provide startups with the resources, tools, and support they need to succeed. This portion of the event offered value to budding entrepreneurs and established startups looking to scale. For example, on day 2, we focused on accelerating innovation with Microsoft Founders Hub and Azure AI. Startups could kickstart their journey with Azure credits and gain access to 30+ tools and services, benefiting from additional credits and offerings as they evolve. It’s a great way for Startups to navigate technical and business challenges. Cloud AI Startup Pitch The Microsoft for Startups AI Pitch Competition and Startup Stage at European Cloud Summit 2025 This was a highly informative and engaging event focused on Artificial Intelligence (AI) and its potential for startups. The Microsoft Startup Programme was introduced as a platform that provides startups with the resources, tools, and support they need to succeed. The AI Empowerment session provided an in-depth overview of the various AI services available through Microsoft Azure AI Foundry and how these cutting-edge technologies can be integrated into business operations. This was perfect for startups looking to get started with AI or those interested in joining the Microsoft Startup Programme. The Spotlight on Innovation session showcased innovative startups from the European Cloud Summit, giving attendees a unique insight into the cutting-edge ideas that are shaping our future. The Empowering Innovation session featured a panel of experts and successful startup founders sharing insights on leveraging Microsoft technologies, navigating the startup ecosystem, and securing funding. This was valuable for budding entrepreneurs or established startups looking to scale. Startup Showcases Holistic AI - End to End AI Governance Platform, Raj Bharat Patel, Securing Advantage in the Era of Agentic AI With this autonomy comes high variability: the difference between a minor efficiency and a major one could mean millions in savings. Conversely, a seemingly small misstep could cascade into catastrophic reputational or compliance risk. The stakes are high—but so is the potential. The next frontier introduces a new paradigm: AI managing AI. As organizations deploy swarms of autonomous agents across business functions, the challenge expands beyond governing human-AI interactions. Now, it's about ensuring that AI agents can monitor, evaluate, and optimize each other—in real time, at scale. This demands a shift in both architecture and mindset: toward native-AI platforms and a new human role, moving from human-in-the-loop to human-on-the-loop—strategically overseeing autonomous systems, not micromanaging them. In this session, Raj Bharat Patel will explore how forward-thinking enterprises can prepare for this emerging reality—building governance and orchestration infrastructures that enable scale, speed, and safety in the age of agentic AI. D-ID | The #1 Choice for AI Generated Video Creation Platform, Yaniv Levi In a world increasingly powered by AI, how do we make digital experiences feel more human? At D-ID we enable businesses to create lifelike, interactive avatars that are transforming the way users communicate with AI, making it more intuitive, personal, and memorable. In this session, we’ll share how our collaboration with Microsoft for Startups helped us scale and innovate, enabling seamless integration with Microsoft’s tools to enhance customer-facing AI agents, and LLM-powered solutions while adding a powerful and personalized new layer of humanlike expression. Startup Pitch Competition Day 2 of the event focused on accelerating innovation with Microsoft Starups and Azure AI Foundry. Startups can kickstart their journey with Azure credits and gain access to 30+ tools and services, benefiting from additional credits and offerings as they evolve. The Azure AI Foundry provides access to an extensive range of AI models, including OpenAI, Meta, Nvidia, Hugging Face. Moreover, startups can navigate through technical and business challenges with the help of free 1:1 sessions with Microsoft experts. The Cloud Startup Stage Competition showcased the most innovative startups in the Microsoft Azure and Azure OpenAI ecosystem, highlighting their groundbreaking solutions and business models. This was a celebration of innovation and success and provided insights into the experiences, challenges, and future plans of these startups The Judges The Pitches . The Winners 1 st Place Graia 2 nd Place iThink365 3 rd Place ShArc Overall, this event was highly informative, engaging, and valuable for anyone interested in AI and its potential for startups. The Microsoft Startup Programme and Azure AI Foundry are powerful tools that can help startups achieve success and transform their ideas into successful businesses. In the end... We are grateful for this year's active and engaging #CollabSummit & #CloudSummit #BizAppSummit— so much goodness, caring, learning, and fun! Great questions, stories, understanding of your concerns, and the sharing in fun. Thank you and see you next year! We look forward to seeing in Cololgne - May 5-7, 2025 – Collaboration Summit (@CollabSummit), Cloud Summit (@EUCloudSummit), and BizApps Summit (@BizAppsSummit) and continue the discussion around AI in our Azure AI Discord CommunityAzure Verified Modules: Support Statement & Target Response Times Update
We are announcing an update to the Azure Verified Modules (AVM) support statement. This change reflects our commitment to providing clarity alongside timely and effective support for our community and AVM module consumers. These changes are in preparation to allow us to enable AVM modules to be published as V1.X.X modules (future announcement on this soon 🥳 sign up to the next AVM Community Call on July 1st 2025 to learn more). What is the new support statement? You can find the support statement on the AVM website here: https://azure.github.io/Azure-Verified-Modules/help-support/module-support/#support-statements For bugs/security issues 5 business days for a triage, meaningful response, and ETA to be provided for fix/resolution by module owner (which could be past the 5 days) For issues that breach the 5 business days, the AVM core team will be notified and will attempt to respond to the issue within an additional 5 business days to assist in triage. For security issues, the Bicep or Terraform Product Groups may step in to resolve security issues, if unresolved, after a further additional 5 business days. For feature requests 15 business days for a meaningful response and initial triage to understand the feature request. An ETA may be provided by the module owner if possible. Key changes from the previous support statement In short its two items: Increasing response time targets from: 3 to 5 business days for issues And from 3 to 5 business days for AVM core team escalation Handling bugs/security issues separately from feature requests Feature requests now have a 15 business day target response time The previous support statement outlined a more rigid structure for issue triage and resolution. It required module owners/contributors to respond within 3 business days, with the AVM core team stepping in if there was no response within a further 24 hours. In the event of a security issue being unaddressed after 5 business days, escalation to the product group (Bicep/Terraform) would occur to assist the AVM core team. There was also no differentiation between bugs/security issues and feature requests, which there now is. You can view the git diff of the support statement here Why the changes? Being honest, we weren't meeting the previous support statement 100% of the time, which we are striving for, across all the AVM modules. And we heard from you that, that wasn't ideal and we agree whole heartedly. Therefore, we took a step back, reflected, looked at the data available and huddled together to redefine what the new AVM support statement and targets should be. "Yeah, but why can't you just meet the previous support statement and targets?" This is a very valid question that you may have or be wondering. And we want to be honest with you so here are the reasons why this isn't possible today: Module owners are not 100% dedicated to only supporting their AVM modules; they also have other daily roles and responsibilities in their jobs at Microsoft. Sometimes this also means conflicting priorities for module owners and they have to make a priority call. We underestimated the impact of holidays, annual leave, public holidays etc. The AVM core teams responsibility is not to resolve all module issues/requests as they are smaller team driving the AVM framework, specs, tooling and tests. They will of course step in when needed, as they have done so far today 👍 We don't get as many contributions from the open-source community as we expected and would still love to see 😉 For clarity we always love to see a Pull Request to help us add new features or resolve bugs and issues, even for simple things like typos. It really does help us go faster 🏃➡️ "How are you going to try and avoid changing (increasing) the support statement and targets in the future?" Again another very valid ask! And we reflected upon this when making these changes to the support statement we are announcing here. To avoid this potential risk we are also taking the following actions today: Building new internal tooling and dashboards for module owners to discover, track and monitor their issues and pull requests across various modules they may own, across multiple languages. (already complete and published 👍) This tooling will also help the AVM core team track issues and report on them more easily to help module owners avoid non-compliance with the targets. Continue to push for, promote, and encourage open-source community contributions Prevent AVM modules being published as V1.X.X if they are unable to prove compliance with the new support statement and targets (sneak peek into V1.X.X requirements) Looking further into the future we are also investigating the following: Building a dedicated AVM team, separate from the AVM core team, that will triage, work on, and fix/resolve issues that are nearing or breaching the support statement and targets. Also they will look into feature requests as and where time allows or are popular/upvoted heavily where module owners are unable to prioritize in the near future due to other priorities. Seeing where AI and other automation tooling can assist with issue triage and resolution to reduce module owner workload. Summary We hope that this provides you with a clear understanding of the changes to the AVM support statement and targets and why we are making these. We also hope you appreciate our honesty on the situation and can see we are taking action to make things better while also reflecting and amending our support statements to be more realistic based on the past 2 years of launching and running AVM to date. Finally we just want to reassure everyone that we remain committed to AVM and have big plans for the rest of the calendar year and beyond! 😎 And with this in mind we want to remind you to sign up to the next AVM Community Call on July 1st 2025 to learn more and ask any questions on this topic or anything else AVM related with the rest of the community 👍 Thanks The AVM Core TeamUsing DeepSeek-R1 on Azure with JavaScript
The pace at which innovative AI models are being developed is outstanding! DeepSeek-R1 is one such model that focuses on complex reasoning tasks, providing a powerful tool for developers to build intelligent applications. The week, we announced its availability on GitHub Models as well as on Azure AI Foundry. In this article, we’ll take a look at how you can deploy and use the DeepSeek-R1 models in your JavaScript applications. TL;DR key takeaways DeepSeek-R1 models focus on complex reasoning tasks, and is not designed for general conversation You can quickly switch your configuration to use Azure AI, GitHub Models, or even local models with Ollama. You can use OpenAI Node SDK or LangChain.js to interact with DeepSeek models. What you'll learn here Deploying DeepSeek-R1 model on Azure. Switching between Azure, GitHub Models, or local (Ollama) usage. Code patterns to start using DeepSeek-R1 with various libraries in TypeScript. Reference links DeepSeek on Azure - JavaScript demos repository Azure AI Foundry OpenAI Node SDK LangChain.js Ollama Requirements GitHub account. If you don't have one, you can create a free GitHub account. You can optionally use GitHub Copilot Free to help you write code and ship your application even faster. Azure account. If you're new to Azure, get an Azure account for free to get free Azure credits to get started. If you're a student, you can also get free credits with Azure for Students. Getting started We'll use GitHub Codespaces to get started quickly, as it provides a preconfigured Node.js environment for you. Alternatively, you can set up a local environment using the instructions found in the GitHub repository. Click on the button below to open our sample repository in a web-based VS Code, directly in your browser: Once the project is open, wait a bit to ensure everything has loaded correctly. Open a terminal and run the following command to install the dependencies: npm install Running the samples The repository contains several TypeScript files under the samples directory that demonstrate how to interact with DeepSeek-R1 models. You can run a sample using the following command: npx tsx samples/<sample>.ts For example, let's start with the first one: npx tsx samples/01-chat.ts Wait a bit, and you should see the response from the model in your terminal. You'll notice that it may take longer than usual to respond, and see a weird response that starts with a <think> tag. This is because DeepSeek-R1 is designed to be used for task that need complex reasoning, like solving problems or answering math questions, and not for you usual chat interactions. Model configuration By default, the repository is configured to use GitHub Models, so you can run any example using Codespaces without any additional setup. While it's great for quick experimentation, GitHub models limit the number of requests you can make in a day and the amount of data you can send in a single request. If you want to use the model more extensively, you can switch to Azure AI or even use a local model with Ollama. You can take a look at the samples/config.ts to see how the different configurations are set up. We'll not cover using Ollama models in this article, but you can find more information in the repository documentation. Deploying DeepSeek-R1 on Azure To experiment with the full capabilities of DeepSeek-R1, you can deploy it on Azure AI Foundry. Azure AI Foundry is a platform that allows you to deploy, manage and develop with AI models quickly. To use Azure AI Foundry, you need to have an Azure account. Let's start by deploying the model on Azure AI Foundry. First, follow this tutorial to deploy a serverless endpoint with the model. When it's time to choose the model, make sure to select the DeepSeek-R1 model in the catalog. Once your endpoint is deployed, you should be able to see your endpoint details and retrieve the URL and API key: Screenshot showing the endpoint details in Azure AI Foundry Then create a .env file in the root of the project and add the following content: AZURE_AI_BASE_URL="https://<your-deployment-name>.<region>.models.ai.azure.com/v1" AZURE_AI_API_KEY="<your-api-key>" Tip: if you're copying the endpoint from the Azure AI Foundry portal, make sure to add the /v1 at the end of the URL. Open the samples/config.ts file and update the default export to use Azure: export default AZURE_AI_CONFIG; Now all samples will use the Azure configuration. Explore reasoning with DeepSeek-R1 Now that you have the model deployed, you can start experimenting with it. Open the samples/08-reasoning.ts file to see how the model handles more complex tasks, like helping us understand a well-known weird piece of code. const prompt = ` float fast_inv_sqrt(float number) { long i; float x2, y; const float threehalfs = 1.5F; x2 = number * 0.5F; y = number; i = *(long*)&y; i = 0x5f3759df - ( i >> 1 ); y = *(float*)&i; y = y * ( threehalfs - ( x2 * y * y ) ); return y; } What is this code doing? Explain me the magic behind it. `; Now run this sample with the command: npx tsx samples/08-reasoning.ts You should see the model's response streaming piece by piece in the terminal, while describing its thought process before providing the actual answer to our question. Screenshot showing the model's response streaming in the terminal Brace yourself, as it might take a while to get the full response! At the end of the process, you should see the model's detailed explanation of the code, along with some context around it. Leveraging frameworks Most examples in this repository are built with the OpenAI Node SDK, but you can also use LangChain.js to interact with the model. This might be especially interested if you need to integrate other sources of data or want to build a more complex application. Open the file samples/07-langchain.ts to have a look at the setup, and see how you can reuse the same configuration we used with the OpenAI SDK. Going further Now it's your turn to experiment and discover the full potential of DeepSeek-R1! You can try more advanced prompts, integrate it into your larger application, or even build agents to make the most out of the model. To continue your learning journey, you can check out the following resources: Generative AI with JavaScript (GitHub): code samples and resources to learn Generative AI with JavaScript. Build a serverless AI chat with RAG using LangChain.js (GitHub): a next step code example to build an AI chatbot using Retrieval-Augmented Generation and LangChain.js.
