Unlocking Your First AI Solution on Azure: Practical Paths for Developers of All Backgrounds
Over the past several months, I’ve spent hundreds of hours working directly with teams—from small startups to mid-market innovators—who share the same aspiration: “We want to use AI, but where do we start?” This question comes up everywhere. It crosses industries, geographies, skill levels, and team sizes. And as developers, we often feel the pressure to “solve AI” end-to-end—model selection, prompt engineering, security, deployment pipelines, integration…. The list is long, and the learning curve can feel even longer. But here’s what we’ve learned through our work in the SMB space and what we recently shared at Microsoft Ignite (Session OD1210). The first mile of AI doesn’t have to be complex. You don’t need an army of engineers, and you don’t need to start from scratch. You just need the right path. In our Ignite on-demand session with UnifyCloud, we demonstrated two fast, developer-friendly ways to get your first AI workload running on Azure—both grounded in real-world patterns we see every day. Path 1: Build Quickly with Microsoft Foundry Templates Microsoft Foundry gives developers pre-built, customizable templates that dramatically reduce setup time. In the session, I walked through how to deploy a fully functioning AI chatbot using: Azure AI Foundry GitHub (via the Azure Samples “Get Started with AI Chat” repo) Azure Cloudshell for deployment And zero specialized infra prep With five lines of code and a few clicks, you can spin up a secure internal chatbot tailored for your business. Want responses scoped to your internal content? Want control over the model, costs, or safety filters? Want to plug in your own data sources like SharePoint, Blob Storage, or uploaded docs? You can do all of that—easily and on your terms. This “build fast” path is ideal for: Developers who want control and extensibility Teams validating AI use cases Scenarios where data governance matters Lightweight experimentation without heavy architecture upfront And most importantly, you can scale it later. Path 2: Buy a Production-Ready Solution from a Trusted Partner Not every team wants to build. Not every team has the time, the resources, or the desire to compose their own AI stack. That’s why we showcased the “buy” path with UnifyCloud’s AI Factory, a Marketplace-listed solution that lets customers deploy mature AI capabilities directly into their Azure environment, complete with optional support, management, and best practices. In the demo, UnifyCloud’s founder Vivek Bhatnagar walked through: How to navigate Microsoft Marketplace How to evaluate solution listings How to review pricing plans and support tiers How to deploy a partner-built AI app with just a few clicks How customers can accelerate their time to value without implementation overhead This path is perfect when you want: A production-ready AI solution A supported, maintained experience Minimal engineering lift Faster time to outcome Why Azure? Why Now? During the session, we also outlined three reasons developers are choosing Azure for their first AI workloads: 1. Secure, governed, safe by design Azure mitigates risk with always-on guardrails and built-in commitments to security, privacy, and policy-based control. 2. Built for production with a complete AI platform From models to agents to tools and data integrations, Azure provides an enterprise-grade environment developers can trust. 3. Developer-first innovation with agentic DevOps Azure puts developers at the center, integrating AI across the software development lifecycle to help teams build faster and smarter. The Session: Build or Buy—Two Paths, One Goal Whether you build using Azure AI Foundry or buy through Marketplace, the goal is the same: Help teams get to their first AI solution quickly, confidently, and securely. You don’t need a massive budget. You don’t need deep ML experience. You don’t need a full-time AI team. What you need is a path that matches your skills, your constraints, and your timeline. Watch the Full Ignite Session You can watch the full session on-demand now also on YouTube: OD1201 — “Unlock Your First AI Solution on Azure” It includes: The full build and buy demos Partner perspectives Deployment walkthroughs And guidance you can take back to your teams today If you want to explore the same build path we showed at Ignite: ➡️ Azure Samples – Get Started with AI Chat https://github.com/Azure-Samples/get-started-with-ai-chat Deploy it, customize it, attach your data sources, and extend it. It’s a great starting point. If you’re curious about the Marketplace path: ➡️ Search for “UnifyCloud AI Factory” on Microsoft Marketplace You’ll see support offerings, solution details, and deployment instructions. Closing Thought The gap between wanting to adopt AI and actually running AI in production is shrinking fast. Azure makes it possible for teams, especially those without deep AI experience, to take meaningful steps today. No perfect architecture required. No million-dollar budget. No wait for a future-state roadmap. Just two practical paths: Build quickly. Buy confidently. Start now. If you have questions, ideas, or want to share what you’re building, feel free to reach out here in the Developer Community. I’d love to hear what you’re creating. — Joshua Huang Microsoft AzureAzure Workbook for ACR tokens and their expiration dates
In this article, we will see how to monitor Azure Container Registry (ACR) tokens with their expiration dates. We will demonstrate how to do this using the Azure REST API: Registries - Tokens - List and an Azure Workbook. To obtain a list of Azure Container Registry (ACR) tokens and their expiration dates using the Azure Resource Manager API, we need to perform a series of REST API calls to authenticate and retrieve the necessary information. This process involves the following steps: Authenticate and obtain an access token. List ACR tokens. Get token credentials and expiration dates.Strategic Solutions for Seamless Integration of Third-Party SaaS
Modern systems must be modular and interoperable by design. Integration is no longer a feature, it’s a requirement. Developers are expected to build architectures that connect easily with third-party platforms, but too often, core systems are designed in isolation. This disconnect creates friction for downstream teams and slows delivery. At Microsoft, SaaS platforms like SAP SuccessFactors and Eightfold support Talent Acquisition by handling functions such as requisition tracking, application workflows, and interview coordination. These tools help reduce costs and free up engineering focus for high-priority areas like Azure and AI. The real challenge is integrating them with internal systems such as Demand Planning, Offer Management, and Employee Central. This blog post outlines a strategy centered around two foundational components: an Integration and Orchestration Layer, and a Messaging Platform. Together, these enable real-time communication, consistent data models, and scalable integration. While Talent Acquisition is the use case here, the architectural patterns apply broadly across domains. Whether you're embedding AI pipelines, managing edge deployments, or building platform services, thoughtful integration needs to be built into the foundation, not bolted on later.Mastering Query Fields in Azure AI Document Intelligence with C#
Introduction Azure AI Document Intelligence simplifies document data extraction, with features like query fields enabling targeted data retrieval. However, using these features with the C# SDK can be tricky. This guide highlights a real-world issue, provides a corrected implementation, and shares best practices for efficient usage. Use case scenario During the cause of Azure AI Document Intelligence software engineering code tasks or review, many developers encountered an error while trying to extract fields like "FullName," "CompanyName," and "JobTitle" using `AnalyzeDocumentAsync`: The error might be similar to Inner Error: The parameter urlSource or base64Source is required. This is a challenge referred to as parameter errors and SDK changes. Most problematic code are looks like below in C#: BinaryData data = BinaryData.FromBytes(Content); var queryFields = new List<string> { "FullName", "CompanyName", "JobTitle" }; var operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, modelId, data, "1-2", queryFields: queryFields, features: new List<DocumentAnalysisFeature> { DocumentAnalysisFeature.QueryFields } ); One of the reasons this failed was that the developer was using `Azure.AI.DocumentIntelligence v1.0.0`, where `base64Source` and `urlSource` must be handled internally. Because the older examples using `AnalyzeDocumentContent` no longer apply and leading to errors. Practical Solution Using AnalyzeDocumentOptions. Alternative Method using manual JSON Payload. Using AnalyzeDocumentOptions The correct method involves using AnalyzeDocumentOptions, which streamlines the request construction using the below steps: Prepare the document content: BinaryData data = BinaryData.FromBytes(Content); Create AnalyzeDocumentOptions: var analyzeOptions = new AnalyzeDocumentOptions(modelId, data) { Pages = "1-2", Features = { DocumentAnalysisFeature.QueryFields }, QueryFields = { "FullName", "CompanyName", "JobTitle" } }; - `modelId`: Your trained model’s ID. - `Pages`: Specify pages to analyze (e.g., "1-2"). - `Features`: Enable `QueryFields`. - `QueryFields`: Define which fields to extract. Run the analysis: Operation<AnalyzeResult> operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, analyzeOptions ); AnalyzeResult result = operation.Value; The reason this works: The SDK manages `base64Source` automatically. This approach matches the latest SDK standards. It results in cleaner, more maintainable code. Alternative method using manual JSON payload For advanced use cases where more control over the request is needed, you can manually create the JSON payload. For an example: var queriesPayload = new { queryFields = new[] { new { key = "FullName" }, new { key = "CompanyName" }, new { key = "JobTitle" } } }; string jsonPayload = JsonSerializer.Serialize(queriesPayload); BinaryData requestData = BinaryData.FromString(jsonPayload); var operation = await client.AnalyzeDocumentAsync( WaitUntil.Completed, modelId, requestData, "1-2", features: new List<DocumentAnalysisFeature> { DocumentAnalysisFeature.QueryFields } ); When to use the above: Custom request formats Non-standard data source integration Key points to remember Breaking changes exist between preview versions and v1.0.0 by checking the SDK version. Prefer `AnalyzeDocumentOptions` for simpler, error-free integration by using built-In classes. Ensure your content is wrapped in `BinaryData` or use a direct URL for correct document input: Conclusion In this article, we have seen how you can use AnalyzeDocumentOptions to significantly improves how you integrate query fields with Azure AI Document Intelligence in C#. It ensures your solution is up-to-date, readable, and more reliable. Staying aware of SDK updates and evolving best practices will help you unlock deeper insights from your documents effortlessly. Reference Official AnalyzeDocumentAsync Documentation. Official Azure SDK documentation. Azure Document Intelligence C# SDK support add-on query field.
Azure Event Grid Domain Creation: Overcoming AZ CLI's TLS Parameter Limitations with Workaround
Introduction: The Intersection of Security Policies and DevOps Automation In the modern cloud landscape, organizations increasingly enforce strict security requirements through platform policies. One common requirement is mandating latest TLS versions for example TLS 1.2 across all deployed resources to protect data in transit. While this is an excellent security practice, it can sometimes conflict with the available configuration options in deployment tools, particularly in the Azure CLI. This blog explores a specific scenario that many Azure DevOps teams encounter: how to deploy an Azure Event Grid domain when your organization has a custom policy requiring latest version considering TLS 1.2, but the Azure CLI command doesn't provide a parameter to configure this setting. The Problem: Understanding the Gap Between Policy and Tooling What Is Azure Event Grid? Azure Event Grid is a serverless event routing service that enables event-driven architectures. It manages the routing of events from various sources (like Azure services, custom applications, or SaaS products) to different handlers such as Azure Functions, Logic Apps, or custom webhooks. An Event Grid domain provides a custom topic endpoint that can receive events from multiple sources, offering a way to organize and manage events at scale. The Policy Requirement: Many organizations implement Azure Policy to enforce security standards across their cloud infrastructure. A common policy might look like this: { "policyRule": { "if": { "allOf": [ { "field": "type", "equals": "Microsoft.EventGrid/domains" }, { "anyOf": [ { "field": "Microsoft.EventGrid/domains/minimumTlsVersion", "exists": false }, { "field": "Microsoft.EventGrid/domains/minimumTlsVersion", "notEquals": "1.2" } ] } ] }, "then": { "effect": "deny" } } } This policy blocks the creation of any Event Grid domain that doesn't explicitly set TLS 1.2 as the minimum TLS version. The CLI Limitation: Now, let's examine the Azure CLI command to create an Event Grid domain: az eventgrid domain | Microsoft Learn TLS property is unrecognized with the latest version of AZ CLI version. Current Status of This Limitation: It's worth noting that this limitation has been recognized by the Azure team. There is an official GitHub feature request tracking this issue, which you can find at => Please add TLS support while creation of Azure Event Grid domain through CLI · Issue #31278 · Azure/azure-cli Before implementing this workaround described in this article, I recommend checking the current status of this feature request. The Azure CLI is continuously evolving, and by the time you're reading this, the limitation might have been addressed. However, as of April 2025, this remains a known limitation in the Azure CLI, necessitating the alternative approach outlined below. Why This Matters: This limitation becomes particularly problematic in CI/CD pipelines or Infrastructure as Code (IaC) scenarios where you want to automate the deployment of Event Grid domain resources. Workaround: You can utilize below ARM template and deploy it through AZ CLI in your deployment pipeline as below: Working ARM template: { "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", "contentVersion": "1.0.0.0", "parameters": { "domainName": { "type": "string", "metadata": { "description": "Name of the Event Grid Domain" } }, "location": { "type": "string", "defaultValue": "[resourceGroup().location]", "metadata": { "description": "Azure region for the domain" } } }, "resources": [ { "type": "Microsoft.EventGrid/domains", "apiVersion": "2025-02-15", "name": "[parameters('domainName')]", "location": "[parameters('location')]", "properties": { "minimumTlsVersionAllowed": "1.2" } } ] } Please note I've used latest API version from below official Microsoft documentation : Microsoft.EventGrid/domains - Bicep, ARM template & Terraform AzAPI reference | Microsoft Learn Working AZ CLI command: az deployment group create --resource-group <rg> --template-file <armtemplate.json> --parameters domainName=<event grid domain name> You can store this ARM template in your configuration directory with replacement for Azure CLI command. It explicitly sets TLS 1.2 for Event Grid domains, ensuring security compliance where the CLI lacks this parameter. For example: az deployment group create --resource-group <rg> --template-file ./config/<armtemplate.json> --parameters domainName=<event grid domain name> Disclaimer: The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you've found this workaround valuable for addressing the Event Grid domain TLS parameter limitation in Azure CLI. 😊Azure Event Grid CLI Identity Gaps & Workarounds with Python REST and ARM Templates
Azure Event Grid has become a cornerstone service for building event-driven architectures in the cloud. It provides a scalable event routing service that enables reactive programming patterns, connecting event sources to event handlers seamlessly. However, when working with Event Grid through the Azure CLI, developers often encounter a significant limitation: the inability to configure system-assigned managed identities using CLI commands. In this blog post, I'll explore this limitation and provide practical workarounds using Python REST API calls and ARM templates with CLI to ensure your Event Grid deployments can leverage the security benefits of managed identities without being blocked by tooling constraints. Problem Statement: Unlike many other Azure resources that support the --identity or ---assign-identity parameter for enabling system-assigned managed identities, Event Grid's CLI commands lack this capability while creating event subscription for system topic at the moment. This means that while the Azure Portal and other methods support managed identities for Event Grid, you can't configure them directly through the CLI in case of system topic event subscriptions For example you can add managed identity for delivery through portal but not through AZ CLI: If you try to use the following CLI command: az eventgrid system-topic event-subscription create \ --name my-sub \ --system-topic-name my-topic \ --resource-group my-rg \ --endpoint <EH resource id> --endpoint-type eventhub \ --identity systemassigned You'll run into a limitation: The --identity flag is not supported or unrecognized for system topic subscriptions in Azure CLI. Also, --delivery-identity is in preview and under development Current Status of This Limitation: It's worth noting that this limitation has been recognized by the Azure team. There is an official GitHub feature request tracking this issue, which you can find at Use managed identity to command creates an event subscription for an event grid system topic · Issue #26910 · Azure/azure-cli. Before implementing any of the workarounds described in this article, I recommend checking the current status of this feature request. The Azure CLI is continuously evolving, and by the time you're reading this, the limitation might have been addressed. However, as of April 2025, this remains a known limitation in the Azure CLI, necessitating the alternative approaches outlined below. Why This Matters: This limitation becomes particularly problematic in CI/CD pipelines or Infrastructure as Code (IaC) scenarios where you want to automate the deployment of Event Grid resources with managed identities. Solution 1: Using Azure REST API with Python request library: The first approach to overcome this limitation is to use the Azure REST API with Python. This provides the most granular control over your Event Grid resources and allows you to enable system-assigned managed identities programmatically. System Topic Event Subscriptions - Create Or Update - REST API (Azure Event Grid) | Microsoft Learn You can retrieve Azure Entra token using below CLI command: az account get-access-token Sample working code & payload: import requests import json subscription_id = <> resource_group = <> system_topic_name = <> event_subscription_name = <> event_hub_resource_id = <> access_token = <> url = f"https://management.azure.com/subscriptions/{subscription_id}/resourceGroups/{resource_group}/providers/Microsoft.EventGrid/systemTopics/{system_topic_name}/eventSubscriptions/{event_subscription_name}?api-version=2024-12-15-preview" payload = { "identity": { "type": "SystemAssigned" }, "properties": { "topic": "/subscriptions/<>/resourceGroups/<>/providers/Microsoft.EventGrid/systemTopics/<>", "filter": { "includedEventTypes": [ "Microsoft.Storage.BlobCreated", "Microsoft.Storage.BlobDeleted" ], "advancedFilters": [], "enableAdvancedFilteringOnArrays": True }, "labels": [], "eventDeliverySchema": "EventGridSchema", "deliveryWithResourceIdentity": { "identity": { "type": "SystemAssigned" }, "destination": { "endpointType": "EventHub", "properties": { "resourceId": "/subscriptions/<>/resourceGroups/rg-sch/providers/Microsoft.EventHub/namespaces/<>/eventhubs/<>", "deliveryAttributeMappings": [ { "name": "test", "type": "Static", "properties": { "value": "test", "isSecret": False, "sourceField": "" } }, { "name": "id", "type": "Dynamic", "properties": { "value": "abc", "isSecret": False, "sourceField": "data.key" } } ] } } } } } headers = { "Authorization": f"Bearer {access_token}", "Content-Type": "application/json" } response = requests.put(url, headers=headers, data=json.dumps(payload)) if response.status_code in [200, 201]: print("Event subscription created successfully!") Remember that these tokens are sensitive security credentials, so handle them with appropriate care. They should never be exposed in logs, shared repositories, or other insecure locations. Solution 2: Using ARM Templates & deploying it through CLI Another solution is to use Azure Resource Manager (ARM) templates, which fully support system-assigned managed identities for Event Grid. This approach works well in existing IaC workflows. Microsoft.EventGrid/systemTopics/eventSubscriptions - Bicep, ARM template & Terraform AzAPI reference | Microsoft Learn Here's a sample ARM template that creates an Event Grid topic with a system-assigned managed identity: { "$schema": "https://schema.management.azure.com/schemas/2019-04-01/deploymentTemplate.json#", "contentVersion": "1.0.0.0", "parameters": { "systemTopicName": { "type": "string", "metadata": { "description": "Name of the existing system topic" } }, "eventSubscriptionName": { "type": "string", "metadata": { "description": "Name of the event subscription to create" } }, "eventHubResourceId": { "type": "string", "metadata": { "description": "Resource ID of the Event Hub to send events to" } }, "includedEventType": { "type": "string", "defaultValue": "Microsoft.Storage.BlobCreated", "metadata": { "description": "Event type to filter on" } } }, "resources": [ { "type": "Microsoft.EventGrid/systemTopics/eventSubscriptions", "apiVersion": "2024-06-01-preview", "name": "[format('{0}/{1}', parameters('systemTopicName'), parameters('eventSubscriptionName'))]", "identity": { "type": "SystemAssigned" }, "properties": { "deliveryWithResourceIdentity": { "destination": { "endpointType": "EventHub", "properties": { "resourceId": "[parameters('eventHubResourceId')]" } }, "identity": { "type": "SystemAssigned" } }, "eventDeliverySchema": "EventGridSchema", "filter": { "includedEventTypes": [ "[parameters('includedEventType')]" ] } } } ] } How to deploy via Azure CLI: az deployment group create \ --resource-group <your-resource-group> \ --template-file eventgridarmtemplate.json \ --parameters \ systemTopicName=<system-topic-name> \ eventSubscriptionName=<event-subscription-name> \ eventHubResourceId="/subscriptions/<sub>/resourceGroups/<rg>/providers/Microsoft.EventHub/namespaces/<namespace>/eventhubs/<hub>" Disclaimer The sample scripts provided in this article are provided AS IS without warranty of any kind. The author is not responsible for any issues, damages, or problems that may arise from using these scripts. Users should thoroughly test any implementation in their environment before deploying to production. Azure services and APIs may change over time, which could affect the functionality of the provided scripts. Always refer to the latest Azure documentation for the most up-to-date information. Thanks for reading this blog! I hope you've found these workarounds valuable for addressing the Event Grid identity parameter limitation in Azure CLI. 😊Elevate Your AI Expertise with Microsoft Azure: Learn Live Series for Developers
Unlock the power of Azure AI and master the art of creating advanced AI agents. Starting from April 15th, embark on a comprehensive learning journey designed specifically for professional developers like you. This series will guide you through the official Microsoft Learn Plan, focused on the latest agentic AI technologies and innovations. Generative AI has evolved to become an essential tool for crafting intelligent applications, and AI agents are leading the charge. Here's your opportunity to deepen your expertise in building powerful, scalable agent-based solutions using the Azure AI Foundry, Azure AI Agent Service, and the Semantic Kernel Framework. Why Attend? This Learn Live series will provide you with: In-depth Knowledge: Understand when to use AI agents, how they function, and the best practices for building them on Azure. Hands-On Experience: Gain practical skills to develop, deploy, and extend AI agents with Azure AI Agent Service and Semantic Kernel SDK. Expert Insights: Learn directly from Microsoft’s AI professionals, ensuring you're at the cutting edge of agentic AI technologies. Session Highlights Plan and Prepare AI Solutions | April 15th Explore foundational principles for creating secure and responsible AI solutions. Prepare your development environment for seamless integration with Azure AI services. Fundamentals of AI Agents | April 22nd Discover the transformative role of language models and generative AI in enabling intelligent applications. Understand Microsoft Copilot and effective prompting techniques for agent development. Azure AI Agent Service: Build and Integrate | April 29th Dive into the key features of Azure AI Agent Service. Build agents and learn how to integrate them into your applications for enhanced functionality. Extend with Custom Tools | May 6th Enhance your agents’ capabilities with custom tools, tailored to meet unique application requirements. Develop an AI agent with Semantic Kernel | May 8th Use Semantic Kernel to connect to an Azure AI Foundry project Create Azure AI Agent Service agents using the Semantic Kernel SDK Integrate plugin functions with your AI agent Orchestrate Multi-Agent Solutions with Semantic Kernel | May 13th Utilize the Semantic Kernel SDK to create collaborative multi-agent systems. Develop and integrate custom plugin functions for versatile AI solutions. What You’ll Achieve By the end of this series, you'll: Build AI agents using cutting-edge Azure technologies. Integrate custom tools to extend agent capabilities. Develop multi-agent solutions with advanced orchestration. How to Join Don't miss out on this opportunity to level up your development skills and lead the next wave of AI-driven applications. Register now and set yourself apart as a developer equipped to harness the full potential of Azure AI. 🔗 Register for the Learn Live Series 🗓️ Format: Livestream | Language: English | Topic: Core AI Development Take the leap and transform how you develop intelligent applications with Microsoft Azure AI. Does this revision align with your vision for the blog? Let me know if there's anything else you'd like to refine or add!The Startup Stage: Powered by Microsoft for Startups at European AI & Cloud Summit
🚀 The Startup Stage: Powered by Microsoft for Startups Take center stage in the AI and Cloud Startup Program, designed to showcase groundbreaking solutions and foster collaboration between ambitious startups and influential industry leaders. Whether you're looking to engage with potential investors, connect with clients, or share your boldest ideas, this is the platform to shine. Why Join the Startup Stage? Pitch to Top Investors: Present your ideas and products to key decision-makers in the tech world. Gain Visibility: Showcase your startup in a vibrant space dedicated to innovation, and prove that you are the next game-changer. Learn from the Best: Hear from visionary thought leaders and Microsoft AI experts about the latest trends and opportunities in AI and cloud. AI Competition: Propel Your Startup Stand out from the crowd by participating in the European AI & Cloud Startup Stage competition, exclusively designed for startups leveraging Microsoft AI and Azure Cloud services. Compete for prestigious awards, including: $25,000 in Microsoft Azure Credits. A mentoring session with Marco Casalaina, VP of Products at Azure AI. Fast-track access to exclusive resources through the Microsoft for Startups Program. Get ready to deliver a pitch in front of a live audience and an expert panel on 28 May 2025! How to Apply: Ensure your startup solution runs on Microsoft AI and Azure Cloud. Register as a conference and submit your Competiton application form before the deadline: 14 April 2025 at European Cloud and AI Summit. Be Part of Something Bigger This isn’t just an exhibition—it’s a thriving community where innovation meets opportunity. Don’t miss out! With tickets already 70% sold out, now’s the time to secure your spot. Join the European AI and Cloud Startup Area with a booth or launchpad, and accelerate your growth in the tech ecosystem. Visit the [European AI and Cloud Summit](https://ecs.events) website to learn more, purchase tickets, or apply for the AI competition. Download the sponsorship brochure for detailed insights into this once-in-a-lifetime event. Together, let’s shape the future of cloud technology. See you in Düsseldorf! 🎉Kickstart projects with azd Templates
Navigating today’s software development challenges requires streamlined tools and frameworks. The Azure Developer CLI (azd) simplifies provisioning and deployment on Azure with its intuitive, developer-focused commands. Beyond mere automation, azd templates provide reusable blueprints for proof-of-concept projects, complete configurations for managed systems, and robust Infrastructure as Code assets. By accelerating application development and eliminating redundant setup, azd enables developers to focus on innovation. Embrace azd to enhance productivity and adapt to the evolving development landscape effortlessly.
