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.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.
Getting Started with the AI Dev Gallery
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 Visual Studio 2022 You will need Visual Studio 2022 with the Windows Application Development workload. Running the Gallery To run the gallery: Clone the repository: git clone https://github.com/microsoft/AI-Dev-Gallery.git Run the solution: .\AIDevGallery.sln Hit F5 to build and run the Gallery 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!GitHub Copilot for Azure: 6 Must-Try Features
Ready to supercharge your Azure game right within GitHub Copilot? Dive into our latest blog where we break down six must-try features of GitHub Copilot for Azure. From deploying containers and managing AI models to exploring resources and planning migrations, we've got you covered. Check out the videos to see great examples of how GitHub Copilot for Azure can make your cloud projects smoother and more efficient.
