Don’t miss Building Agents with Azure AI Foundry and Azure AI Foundry Agent Service!
Our dynamic four-part webinar series, Agentic AI + Copilot Partner Skilling Accelerator, empowers you to harness the Microsoft AI ecosystem to unlock new revenue streams and enhance customer success. Across the four sessions, Microsoft partners can expect to learn how to apply AI tools in no-code, low-code, and pro-code scenarios to build intelligent chat and workflow solutions, extend and customize capabilities, and create advanced, custom AI functionality. Don't miss the final session in the series, Building Agents with Azure AI Foundry and Azure AI Foundry Agent Service, where you'll learn how to design and deploy intelligent agents with Azure AI Foundry and Azure AI Foundry Agent Service, including multi-agent architectures and key protocols such as A2A and MCP. The live virtual event is scheduled for December 15, 2025. Register today to reserve your spot! Be sure to follow this Partner news blog for all partner related announcements by clicking follow above!SubgenAI makes AI practical, scalable, and sustainable with Azure Database for PostgreSQL
Authors: Abe Omorogbe, Senior Program Manager at Microsoft and Julia Schröder Langhaeuser, VP of Product Serenity Star at SubgenAI AI agents are thriving in pilots and prototypes. However, scaling them across organizations is more difficult. A recent MIT report shows that 95 percent of projects fail to reach production. Long development cycles, lack of observability, and compliance hurdles leave enterprises struggling to deliver production-ready agents. SubgenAI, a European generative AI company that focuses on democratizing AI for businesses and governments, saw an opportunity to change this. Its flagship platform, Serenity Star, transforms AI agent development from a code-heavy, fragmented process into a streamlined, no-code experience. Built on Microsoft Azure Database for PostgreSQL, Semantic Kernel, and Microsoft Foundry, Serenity Star empowers organizations to deploy production-grade AI agents in minutes, not months. SubgenAI’s mission is to make generative AI accessible, scalable, and secure for every organization. Whether you're a startup or a multinational, Serenity Star offers the tools to build intelligent agents tailored to your business logic, with full control over data and deployment. “Many things must happen around it in the coming years. Serenity Star is designed to solve problems like data control, compliance, and decision ethics—so companies can unleash the full potential of generative AI without compromising trust or profitability” - Lorenzo Serratosa Simplifying complex AI agent development Technical and operational challenges are inherent in enterprise-wide AI agent deployments. Examples include time-consuming iteration cycles, lack of observability and cost control, security concerns, and data sovereignty requirements. Serenity Star addresses these pain points by handling the entire AI agent lifecycle while providing enterprise-grade security and compliance features. Users can focus on defining their agent's purpose and behavior rather than wrestling with technical implementation details. Its framework focuses on four essentials for AI agents: the brain (underlying model), knowledge (accessible information), behavior (programmed responses), and tools (external system integrations). This framework directly influenced the technology stack choices for Serenity Star, with Azure Database for PostgreSQL powering the knowledge retrieval and Semantic Kernel enabling flexible model orchestration. Real-world architecture in action When a user query comes in, Serenity Star uses the vector capabilities of Azure Database for PostgreSQL to retrieve the most relevant knowledge. That context, combined with the user’s input, forms a complete prompt. Semantic Kernel then routes the request to the right large language model, ensuring the agent delivers accurate and context-aware responses. Serenity Star’s native connectors to platforms such as Microsoft Teams, WhatsApp, and Google Tag Manager are also part of this architecture, delivering answers directly in the collaboration and communication tools enterprises already use every day. Figure 1: Serenity Star Architecture This routing and orchestration architecture applies to the multi-tenant SaaS deployments and dedicated customer instances offered by Serenity Star. Azure Database for PostgreSQL provides native Row-Level Security (RLS) capabilities, a key advantage for securely managing multi-tenant environments. Multi-tenant deployments allow organizations to get started quickly with lower overhead, while dedicated instances meet the needs of enterprises with strict compliance and data sovereignty requirements. Optimizing for scale The same architecture that powers retrieval, routing, and multi-channel delivery also provides a foundation for performance at scale. As adoption grows, the team continuously monitors query volume, response times, and resource efficiency across both multi-tenant and dedicated environments. To stay ahead of demand, SubgenAI actively experiments with new Azure Database for PostgreSQL features such as DiskANN for faster vector search. These optimizations keep latency low even as more users and connectors are added. The result is a platform that maintains sub-60-second response times for 99 percent of chart generations, regardless of deployment model or integration point. With this systematic approach to scaling, organizations can deploy fully functional AI agents that are connected to their preferred communication platforms in just 15 minutes instead of hours. For enterprises that have struggled with failed AI projects, Serenity Star offers not only a secure and compliant solution but also one proven to grow with their needs. Why Azure Database for PostgreSQL is a cornerstone The knowledge component of AI agents relies heavily on retrieval-augmented generation (RAG) systems that perform similarity searches against embedded content. This requires a database capable of handling efficient vector search while maintaining enterprise-grade reliability and security. SubgenAI evaluated multiple vector database options. However, Azure Database for PostgreSQL with PGVector emerged as the clear winner. There were several compelling reasons for this. One is its mature technology, which provides immediate credibility with enterprise customers. Two, the ability to scale GenAI use cases with features like DiskANN for accurate and scalable vector search. There, the flexibility and appeal of using an open-source database with a vibrant and fast-moving community. As CPO Leandro Harillo explains: “When we tell them their data runs on Azure Database for PostgreSQL, it’s a relief. It's a well-known technology versus other options that were born with this new AI revolution.” As an open-source relational database management system, Azure Database for PostgreSQL offers extensibility and seamless integration with Microsoft’s enterprise ecosystem. It has a trusted reputation that appeals to organizations with strict data sovereignty and compliance requirements such as those in healthcare and insurance where reliability and governance are non-negotiable. The integration with Azure's broader ecosystem also simplified implementation. With Serenity Star built entirely on Azure infrastructure, Azure Database for PostgreSQL provided seamless connectivity and consistent performance characteristics. The fast response times necessary for real-time agent interactions are the result, along with maintaining the reliability demanded by enterprise customers. Semantic Kernel: Enabling model flexibility at scale Enterprise AI success requires the ability to experiment with different models and adapt quickly as technology evolves. Semantic Kernel makes this possible, supporting over 300 LLMs and embedding models through a unified interface. With Serenity Star, organizations can make genuine choices about their AI implementations without vendor lock-in. Companies can use embedding models from OpenAI through Azure deployments, ensuring their information remains in their own infrastructure while accessing cutting-edge capabilities. If business requirements change or new models emerge, switching becomes a configuration change rather than a development project. Semantic Kernel's comprehensive connector ecosystem also accelerated SubgenAI's own development process. Interfaces for different vector databases enabled rapid prototyping and comparison during the evaluation phase. “Semantic Kernel helped us to be able to try the different ones and choose the one that fit better for us,” notes Julia Schroder, VP of Product. The SubgenAI team has also extended Semantic Kernel to support more features in Azure Database for PostgreSQL, which is easier because of how well-known and popular PostgreSQL is. SubgenAI has also contributed improvements back to the community. This collaborative approach ensures the platform benefits from the latest developments while helping advance the broader ecosystem. Proven impact of Azure Database for PostgreSQL across industries Because organizations struggle to deliver production-ready agents because of long development cycles, lack of observability, and compliance, the effectiveness of Azure Database for PostgreSQL and other Azure services is reflected in deployment metrics and customer feedback. Production-ready agents typically require around 30 iterations for basic implementations. Complex use cases demand significantly more refinement. One GenAI customer in medical education required over 200 iterations to perfect an agent that evaluates medical students through complex case analysis. Azure PostgreSQL and other Azure services support hour-long iteration cycles rather than week-long sprints, which made this level of refinement economically feasible. Cost efficiency is another significant advantage. SubgenAI provisions and configures models in Microsoft Foundry, which eliminates idling GPU resources while providing detailed cost breakdowns. Users can see exactly how tokens are consumed across prompt text, RAG context, and tool usage, enabling data-driven optimization decisions. Consulting partnerships validate the platform's market position. One consulting firm with 50,000 employees is delighted with the easier implementation, faster deployment, and reliable production performance. Conclusion The combination of Azure Database for PostgreSQL and Semantic Kernel has enabled SubgenAI to address the fundamental challenges that cause 95 percent of enterprise AI projects to fail. Organizations using Serenity Star bypass the traditional barriers of lengthy development cycles, limited observability, and compliance hurdles that typically derail AI initiatives. The platform's architecture delivers measurable results, including a 50 percent reduction in coding time, support for complex agents requiring 200+ iterations, and deployment capabilities that compress months-long projects into 15-minute implementations. Azure Database for PostgreSQL provides the enterprise-grade foundation that customers in regulated industries require, while Semantic Kernel ensures organizations retain flexibility as AI technology evolves. This technological partnership creates a reliable pathway for companies to deploy production-ready AI agents without sacrificing data sovereignty or operational control. Through the reliability of Azure Database for PostgreSQL and the flexibility of Semantic Kernel, Serenity Star delivers an enterprise-ready foundation that makes AI practical, scalable, and sustainable.Partner Case Study | Tiger Analytics
In every industry, preventing equipment downtime and maintaining operational continuity is a top priority. Preventive maintenance—making rounds, doing routine maintenance, and logging data—has long been a manual, tedious process, but with recent advances in AI, predicting and preventing failures and downtime is becoming more manageable than ever before. Tiger Analytics is a solutions integrator and Microsoft partner that implements AI-powered solutions. The organization has all three Solutions Partner designations for Azure, as well as three specializations: AI Platform on Microsoft Azure (formerly AI and Machine Learning on Microsoft Azure), Build AI Apps on Microsoft Azure, and Analytics on Microsoft Azure. They believe in using AI to transform operations for customers across industries. Their commitment to using AI to develop smart solutions empowered them to help a medical device company develop a predictive maintenance solution for their radiation therapy devices. Continue reading here Explore all case studies or submit your own Subscribe to case studies tag to follow all new case study posts. Don't forget to follow this blog to receive email notifications of new stories!Close the Year Strong with Surface for Business Deals
As organizations look to maximize their remaining budget and prepare for 2026, now is the moment to modernize device fleets with Surface for Business. Through December 24, limited-time Surface promotions make it easier to accelerate refresh cycles, strengthen endpoint security, and equip employees with devices that are AI-ready from day one. Surface for Business devices combine productivity-forward design, leading AI capabilities, and Microsoft security at multiple layers. Whether refreshing a subset of users or upgrading entire departments, organizations can close the year with hardware that helps reduce risk, assists in lowering management overhead, and positions teams for the next wave of AI-driven productivity. Secure by Design Surface for Business devices deliver hardware-based protections aligned with Secured-core PC standards. Hardware-based security, advanced firmware protections, and memory-safe drivers help reduce exposure across the stack, providing peace of mind that clears the way for AI innovation. AI-Ready With advanced processors including powerful AI chips on supported models, Surface for Business devices are ready to help employees maximize their skills using AI to drive business forward. From a dedicated Copilot key 1 to Foundry on Windows for developing local agents, these devices provide the foundation for people to achieve their best. Learn more about unlocking AI innovation in our new eBook. Ready to Deploy Surface for Business devices support Windows Autopilot 2 , enabling IT teams to deploy devices directly to employees, preconfigured with corporate profiles and security baselines, without imaging or desk-side setup. Combined with centralized management through Microsoft Intune 3 , organizations can reduce deployment time and help keep endpoints consistent from day one. Make the Most of Year-End Purchasing Opportunities Maximize remaining 2025 budget by exploring end-of-year savings on select Surface for Business devices. Work with your preferred reseller to capitalize on year-end spend, or purchase directly through Microsoft Store in the US 4 to take advantage of available offers that make modernizing your device fleet easier as you prepare for 2026. Resellers can help organizations align device selection, deployment plans, and support needs while optimizing budget utilization. Businesses purchasing through Microsoft Store benefit from fast, free shipping and a 60-day return window on most physical products. 5 Across both channels, Surface for Business offers provide a cost-effective path to refresh devices now rather than deferring upgrades—helping IT leaders complete their roadmap, meet procurement targets, and deliver new value to end users before the new year. Find a reseller [https://www.microsoft.com/surface/business/where-to-buy-microsoft-surface Buy from Microsoft Store US [https://www.microsoft.com/en-us/store/collections/surface-deals-bundles] References Feature availability varies by device and market. See Key Support for details. Windows Autopilot device preparation depends on specific capabilities available in Windows client and Microsoft Entra ID. It also requires a mobile device management (MDM) service such as Microsoft Intune. These capabilities can be obtained through various editions and subscription programs. Additional licenses required, not included with Surface. Offers and promotions vary by market. Terms apply. Microsoft Store only ships to certain countries; see Shipping options, costs, and delivery times - Microsoft Support for details.How AI closes requirements gaps, and how Modern Requirements and Microsoft Marketplace can help
In this guest blog post, Asif Sharif, CEO of Modern Requirements, explores where DevOps workflows fall short and how teams can better manage requirements with Copilot4DevOps, Modern Requirements4DevOps, and Microsoft Azure DevOps.AI Dev Days 2025: Your Gateway to the Future of AI Development
What’s in Store? Day 1 – 10 December: Video Link Building AI Applications with Azure, GitHub, and Foundry Explore cutting-edge topics like: Agentic DevOps Azure SRE Agent Microsoft Foundry MCP Models for AI innovation Day 2 – 11 December Agenda: Video Link Using AI to Boost Developer Productivity Get hands-on with: Agent HQ VS Code & Visual Studio 2026 GitHub Copilot Coding Agent App Modernisation Strategies Why Join? Hands-on Labs: Apply the latest product features immediately. Highlights from Microsoft Ignite & GitHub Universe 2025: Stay ahead of the curve. Global Reach: Local-language workshops for LATAM and EMEA coming soon. You’ll recognise plenty of familiar faces in the lineup – don’t miss the chance to connect and learn from the best! 👉 Register now and share widely across your networks – there’s truly something for everyone! https://aka.ms/ai-dev-daysExploring the Future of AI Agents with Microsoft Foundry
Why Agentic AI Matters AI agents are no longer a distant vision—they’re here and transforming how businesses operate. According to industry analysts: Over 1 billion AI agents are expected to be in use by 2028. 80% of organisations plan to integrate agents within the next 2–3 years. By 2026, 40% of enterprise apps will include task-specific AI agents. Why this surge? Agents address critical challenges such as inefficiencies in manual processes, human error, lack of visibility, and scalability issues. They enable autonomous decision-making, with projections suggesting that by 2028, half of day-to-day work decisions will be made autonomously. From Chatbots to Intelligent Agents As Mary Joe highlighted, early chatbots relied on rigid rules and regular expressions, often leading to frustrating user experiences. The introduction of large language models (LLMs) changed the game, making interactions more natural. But true autonomy, where systems act on our behalf, required more than conversational AI. Agentic AI combines: Reasoning and planning capabilities. Tools and APIs for real-world actions. Memory for learning and improving over time. This evolution moves us beyond simple input-output interactions to intelligent systems that can execute workflows, validate data, and deliver outcomes. Microsoft Foundry: Your Platform for Building Agents Microsoft Foundry offers a Platform-as-a-Service (PaaS) approach for creating AI agents, striking a balance between control and ease of use. Key components include: Model Catalogue: Access models from OpenAI, Anthropic, Mistral, and more. Foundry Agent Service: Build and customise agents with integrated tools. Foundry IQ: Knowledge grounding for accurate responses. Control Plane: Ensures safety, trust, and observability in production. Whether you need full control (Infrastructure-as-a-Service) or simplicity (Software-as-a-Service via Copilot Studio), Foundry provides flexibility for diverse scenarios. What Makes an AI Solution Agentic? Unlike traditional AI apps that perform narrow tasks (e.g., extracting text from receipts), agentic solutions: Analyse inputs using LLMs and system instructions. Integrate tools for actions like file search, code execution, or API calls. Retain memory for contextual learning. Operate autonomously across workflows. Real-World Use Cases Agentic AI unlocks new possibilities across industries: Expense Management: Automate claims and approvals. Employee Onboarding: Personalised learning paths and skills navigation. Customer Support: Intelligent assistants for FAQs and troubleshooting. Data Analytics: Interactive insights and reporting with Fabric agents. Multi-agent systems can coordinate complex tasks, with specialised agents handling subtasks under a central orchestrator. Getting Started with Microsoft Foundry Creating your first agent is simple: Sign in at https://ai.azure.com and create a Foundry project. Select a model (e.g., GPT-4.1 mini) and configure deployment options. Customise instructions to define your agent’s persona and tasks. Add tools like file search or code interpreter for extended functionality. Test and iterate using the agent playground, then export code to Visual Studio Code for deployment. For detailed guidance, explore the https://learn.microsoft.com/training. Follow the skilling plan for this series Plans | Microsoft Learn Join the Community Stay connected and keep learning: Discord: Engage with developers building agents. https://aka.ms/foundry/discord GitHub Discussions: Share ideas and troubleshoot. https://aka.ms/foundrydevs Office Hours: Get direct support from product teams. Final Thoughts Agentic AI is reshaping the way we work, enabling systems to act, learn, and collaborate. With Microsoft Foundry, developers have the tools to build secure, scalable, and intelligent agents today not tomorrow. Join the sessions at https://aka.ms/AzureSkilling-Ignite/25Azure API Management Your Auth Gateway For MCP Servers
The Model Context Protocol (MCP) is quickly becoming the standard for integrating Tools 🛠️ with Agents 🤖 and Azure API Management is at the fore-front, ready to support this open-source protocol 🚀. You may have already encountered discussions about MCP, so let's clarify some key concepts: Model Context Protocol (MCP) is a standardized way, (a protocol), for AI models to interact with external tools, (and either read data or perform actions) and to enrich context for ANY language models. AI Agents/Assistants are autonomous LLM-powered applications with the ability to use tools to connect to external services required to accomplish tasks on behalf of users. Tools are components made available to Agents allowing them to interact with external systems, perform computation, and take actions to achieve specific goals. Azure API Management: As a platform-as-a-service, API Management supports the complete API lifecycle, enabling organizations to create, publish, secure, and analyze APIs with built-in governance, security, analytics, and scalability. New Cool Kid in Town - MCP AI Agents are becoming widely adopted due to enhanced Large Language Model (LLM) capabilities. However, even the most advanced models face limitations due to their isolation from external data. Each new data source requires custom implementations to extract, prepare, and make data accessible for any model(s). - A lot of heavy lifting. Anthropic developed an open-source standard - the Model Context Protocol (MCP), to connect your agents to external data sources such as local data sources (databases or computer files) or remote services (systems available over the internet through e.g. APIs). MCP Hosts: LLM applications such as chat apps or AI assistant in your IDEs (like GitHub Copilot in VS Code) that need to access external capabilities MCP Clients: Protocol clients that maintain 1:1 connections with servers, inside the host application MCP Servers: Lightweight programs that each expose specific capabilities and provide context, tools, and prompts to clients MCP Protocol: Transport layer in the middle At its core, MCP follows a client-server architecture where a host application can connect to multiple servers. Whenever your MCP host or client needs a tool, it is going to connect to the MCP server. The MCP server will then connect to for example a database or an API. MCP hosts and servers will connect with each other through the MCP protocol. You can create your own custom MCP Servers that connect to your or organizational data sources. For a quick start, please visit our GitHub repository to learn how to build a remote MCP server using Azure Functions without authentication: https://aka.ms/mcp-remote Remote vs. Local MCP Servers The MCP standard supports two modes of operation: Remote MCP servers: MCP clients connect to MCP servers over the Internet, establishing a connection using HTTP and Server-Sent Events (SSE), and authorizing the MCP client access to resources on the user's account using OAuth. Local MCP servers: MCP clients connect to MCP servers on the same machine, using stdio as a local transport method. Azure API Management as the AI Auth Gateway Now that we have learned that MCP servers can connect to remote services through an API. The question now rises, how can we expose our remote MCP servers in a secure and scalable way? This is where Azure API Management comes in. A way that we can securely and safely expose tools as MCP servers. Azure API Management provides: Security: AI agents often need to access sensitive data. API Management as a remote MCP proxy safeguards organizational data through authentication and authorization. Scalability: As the number of LLM interactions and external tool integrations grows, API Management ensures the system can handle the load. Security remains to be a critical piece of building MCP servers, as agents will need to securely connect to protected endpoints (tools) to perform certain actions or read protected data. When building remote MCP servers, you need a way to allow users to login (Authenticate) and allow them to grant the MCP client access to resources on their account (Authorization). MCP - Current Authorization Challenges State: 4/10/2025 Recent changes in MCP authorization have sparked significant debate within the community. 🔍 𝗞𝗲𝘆 𝗖𝗵𝗮𝗹𝗹𝗲𝗻𝗴𝗲𝘀 with the Authorization Changes: The MCP server is now treated as both a resource server AND an authorization server. This dual role has fundamental implications for MCP server developers and runtime operations. 💡 𝗢𝘂𝗿 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻: To address these challenges, we recommend using 𝗔𝘇𝘂𝗿𝗲 𝗔𝗣𝗜 𝗠𝗮𝗻𝗮𝗴𝗲𝗺𝗲𝗻𝘁 as your authorization gateway for remote MCP servers. 🔗For an enterprise-ready solution, please check out our azd up sample repo to learn how to build a remote MCP server using Azure API Management as your authentication gateway: https://aka.ms/mcp-remote-apim-auth The Authorization Flow The workflow involves three core components: the MCP client, the APIM Gateway, and the MCP server, with Microsoft Entra managing authentication (AuthN) and authorization (AuthZ). Using the OAuth protocol, the client starts by calling the APIM Gateway, which redirects the user to Entra for login and consent. Once authenticated, Entra provides an access token to the Gateway, which then exchanges a code with the client to generate an MCP server token. This token allows the client to communicate securely with the server via the Gateway, ensuring user validation and scope verification. Finally, the MCP server establishes a session key for ongoing communication through a dedicated message endpoint. Diagram source: https://aka.ms/mcp-remote-apim-auth-diagram Conclusion Azure API Management (APIM) is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). In this blog, we've emphasized the simplicity of connecting AI agents to various data sources through MCP, streamlining previously complex implementations. Given the critical role of secure access to platforms and services for AI agents, APIM offers robust solutions for managing OAuth tokens and ensuring secure access to protected endpoints, making it an invaluable asset for enterprises, despite the challenges of authentication. API Management: An Enterprise Solution for Securing MCP Servers Azure API Management is an essential tool for enterprise customers looking to integrate AI models with external tools using the Model Context Protocol (MCP). It is designed to help you to securely expose your remote MCP servers. MCP servers are still very new, and as the technology evolves, API Management provides an enterprise-ready solution that will evolve with the latest technology. Stay tuned for further feature announcements soon! Acknowledgments This post and work was made possible thanks to the hard work and dedication of our incredible team. Special thanks to Pranami Jhawar, Julia Kasper, Julia Muiruri, Annaji Sharma Ganti Jack Pa, Chaoyi Yuan and Alex Vieira for their invaluable contributions. Additional Resources MCP Client Server integration with APIM as AI gateway Blog Post: https://aka.ms/remote-mcp-apim-auth-blog Sequence Diagram: https://aka.ms/mcp-remote-apim-auth-diagram APIM lab: https://aka.ms/ai-gateway-lab-mcp-client-auth Python: https://aka.ms/mcp-remote-apim-auth .NET: https://aka.ms/mcp-remote-apim-auth-dotnet On-Behalf-Of Authorization: https://aka.ms/mcp-obo-sample 3rd Party APIs – Backend Auth via Credential Manager: Blog Post: https://aka.ms/remote-mcp-apim-lab-blog APIM lab: https://aka.ms/ai-gateway-lab-mcp YouTube Video: https://aka.ms/ai-gateway-lab-demoAmericas & EMEA Fabric Engineering Connection
🚀 Excited to announce the upcoming Fabric Engineering Connection call for Microsoft partners! Join us on Wednesday, December 10, from 8–9 am PT (Americas & EMEA) and Thursday, December 11, from 1–2 am UTC (APAC) for an insightful session featuring Erin Stellato and Mark Brown. This week’s focus: 🎯 GitHub Copilot in SSMS with Fabric SQL 🎯 User-Data Function Integration with Cosmos DB in Fabric Don’t miss the opportunity to learn directly from the experts and discover the latest innovations in Microsoft Fabric. To participate, make sure you’re a member of the Fabric Partner Community Teams Channel. Join here: https://lnkd.in/g_PRdfjt Let’s connect, learn, and shape the future of data together! 💡APAC Fabric Engineering Connection call
🚀 Excited to announce the upcoming Fabric Engineering Connection call for Microsoft partners! Join us on Wednesday, December 10, from 8–9 am PT (Americas & EMEA) and Thursday, December 11, from 1–2 am UTC (APAC) for an insightful session featuring Erin Stellato and Mark Brown. This week’s focus: 🎯 GitHub Copilot in SSMS with Fabric SQL 🎯 User-Data Function Integration with Cosmos DB in Fabric Don’t miss the opportunity to learn directly from the experts and discover the latest innovations in Microsoft Fabric. To participate, make sure you’re a member of the Fabric Partner Community Teams Channel. Join here: https://lnkd.in/g_PRdfjt Let’s connect, learn, and shape the future of data together! 💡
