Azure Landing Zones Accelerators for Bicep and Terraform. Announcing General Availability!
Azure Landing Zones Accelerators are designed to simplify the process of onboarding your Infrastructure as Code into a robust CI / CD pipeline with Azure DevOps or GitHub. Learn more about what the Accelerator can do for you and why you should be using it.Introducing Azure Verified Modules!
Accelerate the delivery Infrastructure-as-Code solutions with Azure Verified Modules! AVM is a community-driven initiative that sets the standards for Infrastructure-as-Code modules within Microsoft and the engaged community. These atomic building blocks encapsulate groups of resources dedicated to one task and are used to deploy Azure resources and their extensions consistently. Stay tuned for more information on how AVM can help you reliably and consistently deliver at scale.Harness any IaC framework with the new extensibility model in Azure Deployment Environments
We’re excited to announce a new extensibility model (now available in public preview) for Azure Deployment Environments that empowers customers to customize their deployment workflows using Bicep, Terraform, Pulumi, or any other infrastructure-as-code (IaC) framework their choice. This new model further streamlines app infrastructure provisioning and makes it easier for platform engineers to meet the unique needs of their organization. Read on to learn more about the extensibility model and what this means for popular frameworks like Bicep and Terraform.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.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.
Getting Started with the AI Dev Gallery
March Update: The Gallery is now available on the Microsoft Store! The AI Dev Gallery is a new open-source project designed to inspire and support developers in integrating on-device AI functionality into their Windows apps. It offers an intuitive UX for exploring and testing interactive AI samples powered by local models. Key features include: Quickly explore and download models from well-known sources on GitHub and HuggingFace. Test different models with interactive samples over 25 different scenarios, including text, image, audio, and video use cases. See all relevant code and library references for every sample. Switch between models that run on CPU and GPU depending on your device capabilities. Quickly get started with your own projects by exporting any sample to a fresh Visual Studio project that references the same model cache, preventing duplicate downloads. Part of the motivation behind the Gallery was exposing developers to the host of benefits that come with on-device AI. Some of these benefits include improved data security and privacy, increased control and parameterization, and no dependence on an internet connection or third-party cloud provider. Requirements Device Requirements Minimum OS Version: Windows 10, version 1809 (10.0; Build 17763) Architecture: x64, ARM64 Memory: At least 16 GB is recommended Disk Space: At least 20GB free space is recommended GPU: 8GB of VRAM is recommended for running samples on the GPU Using the Gallery The AI Dev Gallery has can be navigated in two ways: The Samples View The Models View Navigating Samples In this view, samples are broken up into categories (Text, Code, Image, etc.) and then into more specific samples, like in the Translate Text pictured below: On clicking a sample, you will be prompted to choose a model to download if you haven’t run this sample before: Next to the model you can see the size of the model, whether it will run on CPU or GPU, and the associated license. Pick the model that makes the most sense for your machine. You can also download new models and change the model for a sample later from the sample view. Just click the model drop down at the top of the sample: The last thing you can do from the Sample pane is view the sample code and export the project to Visual Studio. Both buttons are found in the top right corner of the sample, and the code view will look like this: Navigating Models If you would rather navigate by models instead of samples, the Gallery also provides the model view: The model view contains a similar navigation menu on the right to navigate between models based on category. Clicking on a model will allow you to see a description of the model, the versions of it that are available to download, and the samples that use the model. Clicking on a sample will take back over to the samples view where you can see the model in action. Deleting and Managing Models If you need to clear up space or see download details for the models you are using, you can head over the Settings page to manage your downloads: From here, you can easily see every model you have downloaded and how much space on your drive they are taking up. You can clear your entire cache for a fresh start or delete individual models that you are no longer using. Any deleted model can be redownload through either the models or samples view. Next Steps for the Gallery The AI Dev Gallery is still a work in progress, and we plan on adding more samples, models, APIs, and features, and we are evaluating adding support for NPUs to take the experience even further If you have feedback, noticed a bug, or any ideas for features or samples, head over to the issue board and submit an issue. We also have a discussion board for any other topics relevant to the Gallery. The Gallery is an open-source project, and we would love contribution, feedback, and ideation! Happy modeling!Building the Ultimate Nerdland Podcast Chatbot with RAG and LLM: Step-by-Step Guide
Large Language Models (LLMs) are popular in tech. In Belgium and the Netherlands, the podcast "Nerdland" is a favorite for tech and science fans. It covers topics like bioscience, space, robotics, and AI. With over 100 episodes, "Nerdland" is a goldmine of information. So, why not create a chatbot for "Nerdland" fans? This chatbot uses podcast content to engage and inform users. It allows the "Nerdland" community to interact with the content in new ways and makes the information accessible in many languages, thanks to LLMs' multi-language capabilities. This blog post explains the project's technical details, including the LLMs used, integration process, and deployment on Azure.
