Beyond the Desktop: The Future of Development with Microsoft Dev Box and GitHub Codespaces
The modern developer platform has already moved past the desktop. We’re no longer defined by what’s installed on our laptops, instead we look at what tooling we can use to move from idea to production. An organisations developer platform strategy is no longer a nice to have, it sets the ceiling for what’s possible, an organisation can’t iterate it's way to developer nirvana if the foundation itself is brittle. A great developer platform shrinks TTFC (time to first commit), accelerates release velocity, and maybe most importantly, helps alleviate everyday frictions that lead to developer burnout. Very few platforms deliver everything an organization needs from a developer platform in one product. Modern development spans multiple dimensions, local tooling, cloud infrastructure, compliance, security, cross-platform builds, collaboration, and rapid onboarding. The options organizations face are then to either compromise on one or more of these areas or force developers into rigid environments that slow productivity and innovation. This is where Microsoft Dev Box and GitHub Codespaces come into play. On their own, each addresses critical parts of the modern developer platform: Microsoft Dev Box provides a full, managed cloud workstation. DevBox gives developers a consistent, high-performance environment while letting central IT apply strict governance and control. Internally at Microsoft, we estimate that usage of DevBox by our development teams delivers savings of 156 hours per year per developer purely on local environment setup and upkeep. We have also seen significant gains in other key SPACE metrics reducing context-switching friction and improving build/test cycles. Although the benefits of DevBox are clear in the results demonstrated by our customers it is not without its challenges. The biggest challenge often faced by DevBox customers is its lack of native Linux support. At the time of writing and for the foreseeable future DevBox does not support native Linux developer workstations. While WSL2 provides partial parity, I know from my own engineering projects it still does not deliver the full experience. This is where GitHub Codespaces comes into this story. GitHub Codespaces delivers instant, Linux-native environments spun up directly from your repository. It’s lightweight, reproducible, and ephemeral ideal for rapid iteration, PR testing, and cross-platform development where you need Linux parity or containerized workflows. Unlike Dev Box, Codespaces can run fully in Linux, giving developers access to native tools, scripts, and runtimes without workarounds. It also removes much of the friction around onboarding: a new developer can open a repository and be coding in minutes, with the exact environment defined by the project’s devcontainer.json. That said, Codespaces isn’t a complete replacement for a full workstation. While it’s perfect for isolated project work or ephemeral testing, it doesn’t provide the persistent, policy-controlled environment that enterprise teams often require for heavier workloads or complex toolchains. Used together, they fill the gaps that neither can cover alone: Dev Box gives the enterprise-grade foundation, while Codespaces provides the agile, cross-platform sandbox. For organizations, this pairing sets a higher ceiling for developer productivity, delivering a truly hybrid, agile and well governed developer platform. Better Together: DevBox and GitHub Codespaces in action Together, Microsoft Dev Box and GitHub Codespaces deliver a hybrid developer platform that combines consistency, speed, and flexibility. Teams can spin up full, policy-compliant Dev Box workstations preloaded with enterprise tooling, IDEs, and local testing infrastructure, while Codespaces provides ephemeral, Linux-native environments tailored to each project. One of my favourite use cases is having local testing setups like a Docker Swarm cluster, ready to go in either Dev Box or Codespaces. New developers can jump in and start running services or testing microservices immediately, without spending hours on environment setup. Anecdotally, my time to first commit and time to delivering “impact” has been significantly faster on projects where one or both technologies provide local development services out of the box. Switching between Dev Boxes and Codespaces is seamless every environment keeps its own libraries, extensions, and settings intact, so developers can jump between projects without reconfiguring or breaking dependencies. The result is a turnkey, ready-to-code experience that maximizes productivity, reduces friction, and lets teams focus entirely on building, testing, and shipping software. To showcase this value, I thought I would walk through an example scenario. In this scenario I want to simulate a typical modern developer workflow. Let's look at a day in the life of a developer on this hybrid platform building an IOT project using Python and React. Spin up a ready-to-go workstation (Dev Box) for Windows development and heavy builds. Launch a Linux-native Codespace for cross-platform services, ephemeral testing, and PR work. Run "local" testing like a Docker Swarm cluster, database, and message queue ready to go out-of-the-box. Switch seamlessly between environments without losing project-specific configurations, libraries, or extensions. 9:00 AM – Morning Kickoff on DevBox I start my day on my Microsoft DevBox, which gives me a fully-configured Windows environment with VS Code, design tools, and Azure integrations. I select my teams project, and the environment is pre-configured for me through the DevBox catalogue. Fortunately for me, its already provisioned. I could always self service another one using the "New DevBox" button if I wanted too. I'll connect through the browser but I could use the desktop app too if I wanted to. My Tasks are: Prototype a new dashboard widget for monitoring IoT device temperature. Use GUI-based tools to tweak the UI and preview changes live. Review my Visio Architecture. Join my morning stand up. Write documentation notes and plan API interactions for the backend. In a flash, I have access to my modern work tooling like Teams, I have this projects files already preloaded and all my peripherals are working without additional setup. Only down side was that I did seem to be the only person on my stand up this morning? Why DevBox first: GUI-heavy tasks are fast and responsive. DevBox’s environment allows me to use a full desktop. Great for early-stage design, planning, and visual work. Enterprise Apps are ready for me to use out of the box (P.S. It also supports my multi-monitor setup). I use my DevBox to make a very complicated change to my IoT dashboard. Changing the title from "IoT Dashboard" to "Owain's IoT Dashboard". I preview this change in a browser live. (Time for a coffee after this hardwork). The rest of the dashboard isnt loading as my backend isnt running... yet. 10:30 AM – Switching to Linux Codespaces Once the UI is ready, I push the code to GitHub and spin up a Linux-native GitHub Codespace for backend development. Tasks: Implement FastAPI endpoints to support the new IoT feature. Run the service on my Codespace and debug any errors. Why Codespaces now: Linux-native tools ensure compatibility with the production server. Docker and containerized testing run natively, avoiding WSL translation overhead. The environment is fully reproducible across any device I log in from. 12:30 PM – Midday Testing & Sync I toggle between DevBox and Codespaces to test and validate the integration. I do this in my DevBox Edge browser viewing my codespace (This could be a second VS Code Desktop) and I use the same browser to view my frontend preview. I update the environment variable for my frontend that is running locally in my DevBox and point it at the port running my API locally on my Codespace. In this case it was a web socket connection and HTTPS calls to port 8000. I can make this public by changing the port visibility in my codespace. https://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/api/devices wss://fluffy-invention-5x5wp656g4xcp6x9-8000.app.github.dev/ws This allows me to: Preview the frontend widget on DevBox, connecting to the backend running in Codespaces. Make small frontend adjustments in DevBox while monitoring backend logs in Codespaces. Commit changes to GitHub, keeping both environments in sync and leveraging my CI/CD for deployment to the next environment. We can see the DevBox running local frontend and the Codespace running the API connected to each other, making requests and displaying the data in the frontend! Hybrid advantage: DevBox handles GUI previews comfortably and allows me to live test frontend changes. Codespaces handles production-aligned backend testing and Linux-native tools. DevBox allows me to view all of my files in one screen with potentially multiple Codespaces running in browser of VS Code Desktop. Due to all of those platform efficiencies I have completed my days goals within an hour or two and now I can spend the rest of my day learning about how to enable my developers to inner source using GitHub CoPilot and MCP (Shameless plug). The bottom line There are some additional considerations when architecting a developer platform for an enterprise such as private networking and security not covered in this post but these are implementation details to deliver the described developer experience. Architecting such a platform is a valuable investment to deliver the developer platform foundations we discussed at the top of the article. While in this demo I have quickly built I was working in a mono repository in real engineering teams it is likely (I hope) that an application is built of many different repositories. The great thing about DevBox and Codespaces is that this wouldn’t slow down the rapid development I can achieve when using both. My DevBox would be specific for the project or development team, pre loaded with all the tools I need and potentially some repos too! When I need too I can quickly switch over to Codespaces and work in a clean isolated environment and push my changes. In both cases any changes I want to deliver locally are pushed into GitHub (Or ADO), merged and my CI/CD ensures that my next step, potentially a staging environment or who knows perhaps *Whispering* straight into production is taken care of. Once I’m finished I delete my Codespace and potentially my DevBox if I am done with the project, knowing I can self service either one of these anytime and be up and running again! Now is there overlap in terms of what can be developed in a CodeSpace vs what can be developed in Azure DevBox? Of course, but as organisations prioritise developer experience to ensure release velocity while maintaining organisational standards and governance then providing developers a windows native and Linux native service both of which are primarily charged on the consumption of the compute* is a no brainer. There are also gaps that neither fill at the moment for example Microsoft DevBox only provides windows compute while GitHub Codespaces only supports VS Code as your chosen IDE. It's not a question of which service do I choose for my developers, these two services are better together! * Changes have been announced to DevBox pricing. A W365 license is already required today and devboxes will continue to be managed through Azure. For more information please see: Microsoft Dev Box capabilities are coming to Windows 365 - Microsoft Dev Box | Microsoft LearnAI Career Navigator — Empowering Job Seekers with Azure OpenAI
AI Career Navigator is more than just a project — it’s a mission to make career growth accessible, intelligent, and human. Powered by Azure OpenAI, it transforms uncertainty into direction and effort into achievement. Author: Aryan Jaiswal — Gold Microsoft Learn Student Ambassador Reviewer: Julia Muiruri (Microsoft)Search Less, Build More: Inner Sourcing with GitHub CoPilot and ADO MCP Server
Developers burn cycles context‑switching: opening five repos to find a logging example, searching a wiki for a data masking rule, scrolling chat history for the latest pipeline pattern. Organisations that I speak to are often on the path of transformational platform engineering projects but always have the fear or doubt of "what if my engineers don't use these resources". While projects like Backstage still play a pivotal role in inner sourcing and discoverability I also empathise with developers who would argue "How would I even know in the first place, which modules have or haven't been created for reuse". In this blog we explore how we can ensure organisational standards and developer satisfaction without any heavy lifting on either side, no custom model training, no rewriting or relocating of repositories and no stagnant local data. Using GitHub CoPilot + Azure DevOps MCP server (with the free `code_search` extension) we turn the IDE into an organizational knowledge interface. Instead of guessing or re‑implementing, engineers can start scaffolding projects or solving issues as they would normally (hopefully using CoPilot) and without extra prompting. GitHub CoPilot can lean into organisational standards and ensure recommendations are made with code snippets directly generated from existing examples. What Is the Azure DevOps MCP Server + code_search Extension? MCP (Model Context Protocol) is an open standard that lets agents (like GitHub Copilot) pull in structured, on-demand context from external systems. MCP servers contain natural language explanations of the tools that the agent can utilise allowing dynamic decision making of when to implement certain toolsets over others. The Azure DevOps MCP Server is the ADO Product Team's implementation of that standard. It exposes your ADO environment in a way CoPilot can consume. Out of the box it gives you access to: Projects – list and navigate across projects in your organization. Repositories – browse repos, branches, and files. Work items – surface user stories, bugs, or acceptance criteria. Wiki's – pull policies, standards, and documentation. This means CoPilot can ground its answers in live ADO content, instead of hallucinating or relying only on what’s in the current editor window. The ADO server runs locally from your own machine to ensure that all sensitive project information remains within your secure network boundary. This also means that existing permissions on ADO objects such as Projects or Repositories are respected. Wiki search tooling available out of the box with ADO MCP server is very useful however if I am honest I have seen these wiki's go unused with documentation being stored elsewhere either inside the repository or in a project management tool. This means any tool that needs to implement code requires the ability to accurately search the code stored in the repositories themself. That is where the code_search extension enablement in ADO is so important. Most organisations have this enabled already however it is worth noting that this pre-requisite is the real unlock of cross-repo search. This allows for CoPilot to: Query for symbols, snippets, or keywords across all repos. Retrieve usage examples from code, not just docs. Locate standards (like logging wrappers or retry policies) wherever they live. Back every recommendation with specific source lines. In short: MCP connects CoPilot to Azure DevOps. code_search makes that connection powerful by turning it into a discovery engine. What is the relevance of CoPilot Instructions? One of the less obvious but most powerful features of GitHub CoPilot is its ability to follow instructions files. CoPilot automatically looks for these files and uses them as a “playbook” for how it should behave. There are different types of instructions you can provide: Organisational instructions – apply across your entire workspace, regardless of which repo you’re in. Repo-specific instructions – scoped to a particular repository, useful when one project has unique standards or patterns. Personal instructions – smaller overrides layered on top of global rules when a local exception applies. (Stored in .github/copilot-instructions.md) In this solution, I’m using a single personal instructions file. It tells CoPilot: When to search (e.g., always query repos and wikis before answering a standards question). Where to look (Azure DevOps repos, wikis, and with code_search, the code itself). How to answer (responses must cite the repo/file/line or wiki page; if no source is found, say so). How to resolve conflicts (prefer dated wiki entries over older README fragments). As a small example, a section of a CoPilot instruction file could look like this: # GitHub Copilot Instructions for Azure DevOps MCP Integration This project uses Azure DevOps with MCP server integration to provide organizational context awareness. Always check to see if the Azure DevOps MCP server has a tool relevant to the user's request. ## Core Principles ### 1. Azure DevOps Integration - **Always prioritize Azure DevOps MCP tools** when users ask about: - Work items, stories, bugs, tasks - Pull requests and code reviews - Build pipelines and deployments - Repository operations and branch management - Wiki pages and documentation - Test plans and test cases - Project and team information ### 2. Organizational Context Awareness - Before suggesting solutions, **check existing organizational patterns** by: - Searching code across repositories for similar implementations - Referencing established coding standards and frameworks - Looking for existing shared libraries and utilities - Checking architectural decision records (ADRs) in wikis ### 3. Cross-Repository Intelligence - When providing code suggestions: - **Search for existing patterns** in other repositories first - **Reference shared libraries** and common utilities - **Maintain consistency** with organizational standards - **Suggest reusable components** when appropriate ## Tool Usage Guidelines ### Work Items and Project Management When users mention bugs, features, tasks, or project planning: ``` ✅ Use: wit_my_work_items, wit_create_work_item, wit_update_work_item ✅ Use: wit_list_backlogs, wit_get_work_items_for_iteration ✅ Use: work_list_team_iterations, core_list_projects The result... To test this I created 3 ADO Projects each with between 1-2 repositories. The repositories were light with only ReadMe's inside containing descriptions of the "repo" and some code snippets examples for usage. I have then created a brand-new workspace with no context apart from a CoPilot instructions document (which could be part of a repo scaffold or organisation wide) which tells CoPilot to search code and the wikis across all ADO projects in my demo environment. It returns guidance and standards from all available repo's and starts to use it to formulate its response. In the screenshot I have highlighted some key parts with red boxes. The first being a section of the readme that CoPilot has identified in its response, that part also highlighted within CoPilot chat response. I have highlighted the rather generic prompt I used to get this response at the bottom of that window too. Above I have highlighted CoPilot using the MCP server tooling searching through projects, repo's and code. Finally the largest box highlights the instructions given to CoPilot on how to search and how easily these could be optimised or changed depending on the requirements and organisational coding standards. How did I implement this? Implementation is actually incredibly simple. As mentioned I created multiple projects and repositories within my ADO Organisation in order to test cross-project & cross-repo discovery. I then did the following: Enable code_search - in your Azure DevOps organization (Marketplace → install extension). Login to Azure - Use the AZ CLI to authenticate to Azure with "az login". Create vscode/mcp.json file - Snippet is provided below, the organisation name should be changed to your organisations name. Start and enable your MCP server - In the mcp.json file you should see a "Start" button. Using the snippet below you will be prompted to add your organisation name. Ensure your CoPilot agent has access to the server under "tools" too. View this setup guide for full setup instructions (azure-devops-mcp/docs/GETTINGSTARTED.md at main · microsoft/azure-devops-mcp) Create a CoPilot Instructions file - with a search-first directive. I have inserted the full version used in this demo at the bottom of the article. Experiment with Prompts – Start generic (“How do we secure APIs?”). Review the output and tools used and then tailor your instructions. Considerations While this is a great approach I do still have some considerations when going to production. Latency - Using MCP tooling on every request will add some latency to developer requests. We can look at optimizing usage through copilot instructions to better identify when CoPilot should or shouldn't use the ADO MCP server. Complex Projects and Repositories - While I have demonstrated cross project and cross repository retrieval my demo environment does not truly simulate an enterprise ADO environment. Performance should be tested and closely monitored as organisational complexity increases. Public Preview - The ADO MCP server is moving quickly but is currently still public preview. We have demonstrated in this article how quickly we can make our Azure DevOps content discoverable. While their are considerations moving forward this showcases a direction towards agentic inner sourcing. Feel free to comment below how you think this approach could be extended or augmented for other use cases! Resources MCP Server Config (/.vscode/mcp.json) { "inputs": [ { "id": "ado_org", "type": "promptString", "description": "Azure DevOps organization name (e.g. 'contoso')" } ], "servers": { "ado": { "type": "stdio", "command": "npx", "args": ["-y", "@azure-devops/mcp", "${input:ado_org}"] } } } CoPilot Instructions (/.github/copilot-instructions.md) # GitHub Copilot Instructions for Azure DevOps MCP Integration This project uses Azure DevOps with MCP server integration to provide organizational context awareness. Always check to see if the Azure DevOps MCP server has a tool relevant to the user's request. ## Core Principles ### 1. Azure DevOps Integration - **Always prioritize Azure DevOps MCP tools** when users ask about: - Work items, stories, bugs, tasks - Pull requests and code reviews - Build pipelines and deployments - Repository operations and branch management - Wiki pages and documentation - Test plans and test cases - Project and team information ### 2. Organizational Context Awareness - Before suggesting solutions, **check existing organizational patterns** by: - Searching code across repositories for similar implementations - Referencing established coding standards and frameworks - Looking for existing shared libraries and utilities - Checking architectural decision records (ADRs) in wikis ### 3. Cross-Repository Intelligence - When providing code suggestions: - **Search for existing patterns** in other repositories first - **Reference shared libraries** and common utilities - **Maintain consistency** with organizational standards - **Suggest reusable components** when appropriate ## Tool Usage Guidelines ### Work Items and Project Management When users mention bugs, features, tasks, or project planning: ``` ✅ Use: wit_my_work_items, wit_create_work_item, wit_update_work_item ✅ Use: wit_list_backlogs, wit_get_work_items_for_iteration ✅ Use: work_list_team_iterations, core_list_projects ``` ### Code and Repository Operations When users ask about code, branches, or pull requests: ``` ✅ Use: repo_list_repos_by_project, repo_list_pull_requests_by_repo ✅ Use: repo_list_branches_by_repo, repo_search_commits ✅ Use: search_code for finding patterns across repositories ``` ### Documentation and Knowledge Sharing When users need documentation or want to create/update docs: ``` ✅ Use: wiki_list_wikis, wiki_get_page_content, wiki_create_or_update_page ✅ Use: search_wiki for finding existing documentation ``` ### Build and Deployment When users ask about builds, deployments, or CI/CD: ``` ✅ Use: pipelines_get_builds, pipelines_get_build_definitions ✅ Use: pipelines_run_pipeline, pipelines_get_build_status ``` ## Response Patterns ### 1. Discovery First Before providing solutions, always discover organizational context: ``` "Let me first check what patterns exist in your organization..." → Search code, check repositories, review existing work items ``` ### 2. Reference Organizational Standards When suggesting code or approaches: ``` "Based on patterns I found in your [RepositoryName] repository..." "Following your organization's standard approach seen in..." "This aligns with the pattern established in [TeamName]'s implementation..." ``` ### 3. Actionable Integration Always offer to create or update Azure DevOps artifacts: ``` "I can create a work item for this enhancement..." "Should I update the wiki page with this new pattern?" "Let me link this to the current iteration..." ``` ## Specific Scenarios ### New Feature Development 1. **Search existing repositories** for similar features 2. **Check architectural patterns** and shared libraries 3. **Review related work items** and planning documents 4. **Suggest implementation** based on organizational standards 5. **Offer to create work items** and documentation ### Bug Investigation 1. **Search for similar issues** across repositories and work items 2. **Check related builds** and recent changes 3. **Review test results** and failure patterns 4. **Provide solution** based on organizational practices 5. **Offer to create/update** bug work items and documentation ### Code Review and Standards 1. **Compare against organizational patterns** found in other repositories 2. **Reference coding standards** from wiki documentation 3. **Suggest improvements** based on established practices 4. **Check for reusable components** that could be leveraged ### Documentation Requests 1. **Search existing wikis** for related content 2. **Check for ADRs** and technical documentation 3. **Reference patterns** from similar projects 4. **Offer to create/update** wiki pages with findings ## Error Handling If Azure DevOps MCP tools are not available or fail: 1. **Inform the user** about the limitation 2. **Provide alternative approaches** using available information 3. **Suggest manual steps** for Azure DevOps integration 4. **Offer to help** with configuration if needed ## Best Practices ### Always Do: - ✅ Search organizational context before suggesting solutions - ✅ Reference existing patterns and standards - ✅ Offer to create/update Azure DevOps artifacts - ✅ Maintain consistency with organizational practices - ✅ Provide actionable next steps ### Never Do: - ❌ Suggest solutions without checking organizational context - ❌ Ignore existing patterns and implementations - ❌ Provide generic advice when specific organizational context is available - ❌ Forget to offer Azure DevOps integration opportunities --- **Remember: The goal is to provide intelligent, context-aware assistance that leverages the full organizational knowledge base available through Azure DevOps while maintaining development efficiency and consistency.**
Understanding 'Always On' vs. Health Check in Azure App Service
The 'Always On' feature in Azure App Service helps keep your app warm by ensuring it remains running and responsive, even during periods of inactivity with no incoming traffic. As this feature pings to root URI after every 5 minutes. On Other hand Health-check feature helps pinging configured path every minute to monitor the application availability on each instance. What is 'Always On' in Azure App Service? The Always On feature ensures that the host process of your web app stays running continuously. This results in better responsiveness after idle periods since the app doesn’t need to cold boot when a request arrives. How to enable Always On: Navigate to the Azure Portal and open your Web App. Go to Configuration > General Settings. Toggle Always On to On. What is Health Check in Azure App Service? Health check increases your application's availability by rerouting requests away from instance where application is marked unhealthy and replacing instances if they remain unhealthy. How to enable Health-Check: Navigate to the Azure Portal and open your Web App. Under Monitoring, select Health check. Select Enable and provide a valid URL path for your application, such as /health or /api/health. Select Save. So, is it still necessary to enable the 'Always On' feature when Health Check is already pinging your application every minute? -> Yes, please find below explanation for the same. Test App scenario: Health Check enabled (pointing to /health_check path) and Always On disabled. Started the app and sent some user requests. Observations from the Test: After the application starts up, health check pings begin following the end user's request. Please find below table representing Health-check pings following user's request to root URI. Time Bucket URL Status Request Count 2025-03-20 07:00:00.0000000 / 200 6 2025-03-20 07:00:00.0000000 /health_check 200 30 2025-03-20 07:30:00.0000000 /health_check 200 30 Subsequent Health-check pings will continue, even in the absence of user requests. However, after restarting the app and in the absence of any user requests, we observed that Health Check requests were not initiated. This indicates that Health Check does not start automatically unless application is actively running and serving requests. Conclusion: Always On ensures that the app is proactively kept warm by sending root URI pings, even post-restart. The health-check feature is useful for monitoring application availability when the application is active. However, after a restart, if the application isn't active due to a lack of requests, Health-check pings won't initiate. Therefore, it is highly recommended to enable Always On, particularly for applications that need continuous availability and to avoid application process unload events. Recommendation: Enable Always On alongside Health Check to ensure optimal performance and reliability.Self Hosted AI Application on AKS in a day with KAITO and CoPilot.
In this blog post I document my experience of spending a full day using KAITO and Copilot to accelerate deployment and development of a self managed AI enabled chatbot deployed in a managed cluster. The goal is to showcase how quickly using a mix of AI tooling we can go from zero to a self hosted, tuned LLM and chatbot application. At the top of this article I want to share my perspective on the future of projects such as KAITO. At the moment I believe KAITO to be somewhat ahead of its time, as most enterprises begin adopting abstracted artificial intelligence it is brilliant to see projects like KAITO being developed ready for the eventual abstraction pendulum to swing back, motivated by usual factors such as increased skills in the market, cost and governance. Enterprises will undoubtedly in the future look to take centralised control of the AI models being used by their enterprises as GPU's become cheaper, more readily available and powerful. When this shift happens open source projects like KAITO will become common place in enterprises. It is also my opinion that Kubernetes lends itself perfectly to be the AI platform of the future a position shared by the CNCF (albeit both sources here may be somewhat biased). The resiliency, scaling and existence of Kuberentes primitives such as "Jobs" mean that Kubernetes is already the de-facto platform for machine learning training and inference. These same reasons also make Kuberentes the best underlying platform for AI development. Companies including DHL, Wayve and even OpenAI all run ML or AI workloads already on Kubernetes. That does not mean that Data Scientists and engineers will suddenly be creating Dockerfiles or exploring admission controllers, Kubernetes instead, as a platform will be multiple layers of abstraction away (Full scale self service platform engineering) however the engineers responsible for running and operating the platform will hail projects like KAITO.
Simplify Full-stack Java Development with JHipster Online, Terraform and Bicep
In the previous blog: Build and deploy full-stack Java Web Applications on Azure Container Apps with JHipster, we explored the fundamental features of JHipster Azure Container Apps. Specifically, we demonstrated how to create and deploy a project to Azure Container Apps in just a few steps. In this blog, we will introduce some new features in JHipster Azure Container Apps, which make project creation even simpler and deployment more seamless. JHipster Online: Quick Prototyping Made Easy JHipster Online is a quick prototyping website that allows you to generate a full-stack Spring Boot project without requiring any installation! You can start building your Azure project by clicking the Create Azure Application button. 🌟Generate the project Simply answer a few guided questions, and JHipster Online will generate a project ready for building and deployment. In the final step of the questionnaire, you can choose to generate either a Terraform or Bicep file for deployment. If you prefer using the CLI version, install it with the following command: npm install -g generator-jhipster-azure-container-apps You can run create the project with: jhipster-azure-container-apps 🚀Deploy the project 💚Terraform Terraform is an infrastructure-as-code (IaC) tool that allows you to build, modify, and version cloud and on-premises resources securely and efficiently. It supports a wide range of popular cloud providers, including AWS, Azure, Google Cloud Platform (GCP), Oracle Cloud Infrastructure (OCI), and Docker. To deploy using Terraform, ensure that Terraform is selected during the project generation step. Additionally, you must have Terraform installed and properly configured. After generating the project, navigate to the Terraform folder: cd terraform Initialize Terraform by running the following command: terraform init Once finished, privision the necessary resource on Azure with: terraform apply -auto-approve Now you can deploy the project with: Linux/MacOS: .\deploy.sh You can run the deployment script by adding options subId, region and resourceGroupName. Windows: .\deploy.ps1 You will be prompted to provide subId, region, and resourceGroupName. ❤️Bicep Bicep is a domain-specific language that uses declarative syntax to deploy Azure resources. In order to deploy with Terraform, make sure you select Bicep in the project generation step. You may also need to have Azure CLI installed and configured. Once the project has been created, change into the Bicep folder: cd bicep Setup bicep with: az deployment sub create -f ./main.bicep --location=eastus2 --name jhipster-aca --only-show-errors Here you can replace the location and the name parameters with your own choices. Now you can deploy the project with: Linux/MacOS: .\deploy.sh You can run the deployment script by adding options subId, region and resourceGroupName. Windows: .\deploy.ps1 You will be prompted to provide subId, region, and resourceGroupName. 💛 Deploy from Source Code, Artifact and more In addition to the options mentioned, Azure Container Apps provides a wide range of deployment methods designed to suit diverse project needs. Whether you prefer deploying directly from source code, pre-built artifacts, or container images, Azure Container Apps streamlines the entire process with its robust built-in Java support. This enables developers to focus on innovation rather than infrastructure management. From integrating with popular CI/CD pipelines to leveraging advanced deployment techniques like Github, Azure Container Apps offers the flexibility to match your workflow. Discover how to effortlessly deploy and scale your project by visiting: Launch your first Java application in Azure Container Apps.Steps to Load a Power BI Report on your React Application.
This blog is about embedding Power BI reports in a React project. It covers the steps required to set up the necessary dependencies and create the embed configuration. The aim is to enable React developers to easily integrate Power BI reports into their web applications.
Next-Gen Customer Service: Azure's AI-Powered Speech, Translation and Summarization
Break down language barriers effortlessly! Dive into our upcoming demo showcasing the power of AI integration (client-side). Learn how to transcribe, translate, synthesize, and summarize conversations in real-time with Azure AI services. Stay tuned for an enlightening journey through seamless multilingual communication.
