Event Sourcing – Als best friend
As AI-powered applications become more sophisticated, the quality, history, and context of data have never been more important. Traditional CRUD-based systems often struggle to provide the rich historical information and traceability that modern AI solutions require. This is where Event Sourcing shines. In this session, we'll explore why Event Sourcing is becoming one of the most valuable architectural patterns for building intelligent, AI-driven applications. By storing every change as an immutable event, developers gain a complete history of business activities, enabling better analytics, auditability, explainability, and AI-powered decision-making. Through practical examples and real-world scenarios, you'll learn how Event Sourcing works, how it differs from traditional data architectures, and why it provides an ideal foundation for machine learning, generative AI, recommendation engines, and intelligent business systems. We'll also discuss the challenges, trade-offs, and best practices for implementing Event Sourcing successfully in modern distributed applications. Whether you are a software developer, architect, cloud engineer, data professional, AI practitioner, or technical leader, this session will help you understand how Event Sourcing can unlock new possibilities for building smarter applications and more capable AI systems. 🔔 Subscribe to the channel and click the bell icon to stay notified about upcoming Microsoft Zero to Hero sessions, expert talks, and hands-on learning content. What you'll learn: ✅ What Event Sourcing is and how it differs from traditional CRUD architectures ✅ Why historical events are valuable for AI and machine learning workloads ✅ How Event Sourcing improves traceability, auditing, and explainability ✅ Building event-driven architectures for intelligent applications ✅ The relationship between Event Sourcing, CQRS, and modern cloud architectures ✅ Common implementation challenges and how to overcome them ✅ Real-world use cases where Event Sourcing powers AI-driven solutions How to connect: 🌐 Microsoft Zero to Hero Website: https://microsofthero.com 💼 Follow Microsoft Zero to Hero on LinkedIn: https://www.linkedin.com/company/microsofthero 🎓 Continue learning with Microsoft Learn: https://learn.microsoft.com 📺 Subscribe for more Microsoft, Azure, AI, Cloud, .NET, Developer Tools, and Microsoft Learn sessions.Integrating Microsoft Entra ID with Aspire
Modern applications require secure and seamless identity management, and Microsoft Entra ID has become a cornerstone for protecting users, applications, and organizational resources. As cloud-native applications continue to evolve, developers need simple and reliable ways to integrate authentication and authorization into their solutions without sacrificing developer productivity. In this session, we'll explore how to integrate Microsoft Entra ID with .NET Aspire to build secure, scalable, and cloud-ready applications. You'll learn how Aspire simplifies application orchestration while enabling developers to incorporate enterprise-grade identity and access management using Microsoft Entra ID. Through practical demonstrations and real-world scenarios, we'll cover application registration, authentication flows, authorization strategies, token management, and securing distributed applications built with Aspire. You'll also learn best practices for managing identities across services, APIs, and user-facing applications while maintaining a strong security posture. Whether you are a .NET developer, cloud architect, platform engineer, security professional, or technical leader, this session will provide the knowledge and practical guidance needed to integrate modern identity solutions into your Aspire-powered applications. 🔔 Subscribe to the channel and click the bell icon to stay notified about upcoming Microsoft Zero to Hero sessions, expert talks, and hands-on learning content. What you'll learn: ✅ Introduction to Microsoft Entra ID and .NET Aspire ✅ How to integrate authentication into Aspire applications ✅ Configuring application registrations and permissions ✅ Implementing authorization for users, APIs, and services ✅ Managing tokens and securing distributed applications ✅ Best practices for identity, security, and governance ✅ Real-world scenarios for enterprise application development How to connect: 🌐 Microsoft Zero to Hero Website: https://microsofthero.com 💼 Follow Microsoft Zero to Hero on LinkedIn: https://www.linkedin.com/company/microsofthero 🎓 Continue learning with Microsoft Learn: https://learn.microsoft.com 📺 Subscribe for more Microsoft, Azure, AI, Cloud, .NET, Developer Tools, and Microsoft Learn sessions.Building accessible applications in .NET MAUI
Accessibility is not just a feature—it's a fundamental part of building modern applications. By designing with accessibility in mind, developers can create experiences that are usable by everyone, including people with visual, auditory, motor, and cognitive disabilities. The good news is that .NET MAUI provides powerful tools and capabilities to help developers build inclusive applications across multiple platforms. In this session, we'll explore how to build accessible applications using .NET MAUI and learn practical techniques for creating user experiences that work for all users. You'll discover how accessibility features such as screen readers, semantic properties, keyboard navigation, color contrast, scalable text, and assistive technologies can be integrated into your applications from the start. Through real-world examples and demonstrations, we'll cover accessibility best practices, common pitfalls to avoid, and platform-specific considerations across Windows, Android, iOS, and macOS. You'll leave with actionable guidance for designing, developing, and testing applications that meet accessibility standards while delivering a better experience for everyone. Whether you are a .NET developer, mobile developer, UI/UX designer, software architect, or technical leader, this session will help you build more inclusive, user-friendly, and compliant applications with .NET MAUI. 🔔 Subscribe to the channel and click the bell icon to stay notified about upcoming Microsoft Zero to Hero sessions, expert talks, and hands-on learning content. What you'll learn: ✅ Why accessibility matters in modern application development ✅ Accessibility features and capabilities available in .NET MAUI ✅ How to support screen readers and assistive technologies ✅ Best practices for keyboard navigation and focus management ✅ How to design for color contrast, readability, and scalable text ✅ Techniques for testing and validating application accessibility ✅ Common accessibility mistakes and how to avoid them How to connect: 🌐 Microsoft Zero to Hero Website: https://microsofthero.com 💼 Follow Microsoft Zero to Hero on LinkedIn: https://www.linkedin.com/company/microsofthero 🎓 Continue learning with Microsoft Learn: https://learn.microsoft.com 📺 Subscribe for more Microsoft, Azure, AI, Cloud, .NET, Developer Tools, and Microsoft Learn sessions.C# Abstractions, the lies they tell us, and the fact your likely still doing it wrong
Abstractions are one of the most powerful concepts in software engineering. They help us manage complexity, improve maintainability, and write cleaner code. But not all abstractions are created equal. In fact, some abstractions can hide important details, introduce unnecessary complexity, and lead developers down architectural paths that create more problems than they solve. In this session, we'll take a practical and sometimes uncomfortable look at C# abstractions, the assumptions they encourage, and the common mistakes developers continue to make when designing applications. We'll explore how interfaces, dependency injection, repository patterns, service layers, and other popular abstraction techniques are often misunderstood, overused, or applied without fully understanding their trade-offs. Through real-world examples and lessons learned from production environments, you'll discover when abstractions add value, when they become harmful, and how to make better architectural decisions that improve both code quality and developer productivity. Whether you are a C# developer, software engineer, architect, technical lead, or aspiring developer, this session will challenge conventional thinking and help you build more maintainable, efficient, and pragmatic .NET applications. 🔔 Subscribe to the channel and click the bell icon to stay notified about upcoming Microsoft Zero to Hero sessions, expert talks, and hands-on learning content. What you'll learn: ✅ What software abstractions really are and why they matter ✅ Common misconceptions about interfaces and dependency injection ✅ When abstraction improves design—and when it makes things worse ✅ The hidden costs of over-engineering and unnecessary layers ✅ How to identify and avoid common architectural anti-patterns ✅ Practical guidelines for designing maintainable C# applications ✅ Real-world examples of abstraction successes and failures How to connect: 🌐 Microsoft Zero to Hero Website: https://microsofthero.com 💼 Follow Microsoft Zero to Hero on LinkedIn: https://www.linkedin.com/company/microsofthero 🎓 Continue learning with Microsoft Learn: https://learn.microsoft.com 📺 Subscribe for more Microsoft, Azure, AI, Cloud, .NET, Developer Tools, and Microsoft Learn sessions.Azure HorizonDB: Enterprise-Ready Postgres, Engineered for the AI Era
Affan Dar, Vice President of Engineering, PostgreSQL at Microsoft Charles Feddersen, Partner Director of Program Management, PostgreSQL at Microsoft Today at Microsoft Build, we’re pleased to announce the public preview of Azure HorizonDB, a new enterprise-ready Postgres-compatible database service designed to meet the needs of modern AI applications, alongside a set of enhancements to our PostgreSQL tooling in Visual Studio Code to further streamline the developer experience. Postgres is rapidly solidifying its role as a foundational layer in modern data architectures, with accelerating adoption across industries. For developers, it has become the preferred platform for new application development, driven by its extensible architecture, mature extension ecosystem, and adherence to open standards and APIs. At the same time, enterprises are choosing Postgres to re-platform and modernize existing systems, taking advantage of its ability to support a broad range of operational workloads while enabling advanced capabilities such as vector-based data access all within a single, interoperable platform. A Postgres Platform Grounded in Security, Resilience, Scale, and Performance Azure HorizonDB is purpose-built to meet these demands, combining the flexibility developers expect from Postgres with the operational rigor enterprises require. It extends the core Postgres engine with cloud-native capabilities such as integrated identity, fine-grained network and security controls, and seamless lifecycle management, while preserving full compatibility with the open ecosystem of extensions and tools. At the same time, HorizonDB introduces advanced, natively integrated capabilities like vector data support and AI model management, enabling new classes of intelligent applications without sacrificing transactional integrity or developer productivity. These capabilities are backed by a platform designed for enterprise performance and scale. HorizonDB supports databases up to 128 TB, scales out with up to 15 read replicas for high-throughput workloads, and delivers sub-millisecond commit latency across availability zones for low-latency transactions and high availability. This combination is critical for modern applications that require consistent performance under load, including high-concurrency transactional systems, real-time AI-driven interactions, and globally distributed services. The result is a unified platform that scales from the first line of code to globally distributed, mission-critical systems. Enterprise adoption ultimately depends on trust in the platform itself. Azure HorizonDB delivers this with native integration into Microsoft Entra ID for centralized identity and access control, private endpoints for network isolation, and built-in encryption to protect data at rest and in transit. These capabilities are essential for meeting compliance requirements and enabling organizations to run mission-critical workloads with confidence, without added complexity. This foundation is critical for any application, but it becomes indispensable for AI, where secure access to data and controlled model interaction underpin every intelligent experience. Building on this, HorizonDB introduces a set of integrated AI capabilities designed to bring intelligence directly into the database. Run Fast, Memory-Efficient Vector Search with DiskANN HorizonDB brings high-performance vector search directly into Postgres through DiskANN with spherical quantization. This enables efficient, low-latency similarity search at scale while significantly reducing memory and storage overhead. Spherical quantization works by normalizing vectors and encoding them into compact representations that preserve angular distance, allowing the system to compare vectors efficiently with minimal loss in accuracy. The result is the ability to index and query large embedding datasets within the transactional engine itself, making vector search a first-class capability rather than an external dependency. "HorizonDB is compelling because it brings a PostgreSQL-compatible foundation, AI-native capabilities and enterprise-grade controls closer to the operational data layer." Jennings Balavari, Founder, Opsen AI Build Smarter Apps with Hybrid Search in Postgres HorizonDB supports hybrid search by combining vector similarity through pgvector with full-text search enabled via the pg_textsearch extension, allowing applications to match both semantic meaning and precise keyword relevance in a single query. This enables more accurate, context-aware results, such as blending intent-driven retrieval with exact term matching for search, recommendations, or RAG scenarios. By unifying these capabilities within Postgres, HorizonDB improves result quality while simplifying application design without the need for external search systems. Operationalize AI with Built-In AI Model Management Working with vectors requires models to generate, interpret, and evolve embeddings, making model lifecycle a core part of the application stack. HorizonDB introduces integrated AI model management to simplify how models are registered, versioned, and governed alongside data, including built-in support for generative GPT models and ranking models. For example, GPT models can be used to generate summaries, responses, or structured outputs directly from application data, while ranking models enable relevance scoring for search results or recommendations over vector results. By managing these models alongside the data they operate on, HorizonDB ensures consistency, traceability, and control, creating a unified environment where models and data evolve together. “As we build a multi-tenant, AI-driven commerce platform, HorizonDB has been particularly compelling in two areas: scale and how close AI capabilities are to the data itself. Running vector search, filtering, and model-driven workflows directly inside the database removes a lot of the complexity we’d normally manage across separate services." James Frawley, CIAO, ReFiBuy Bring AI into SQL with AI Functions With models managed in place, AI Functions provide a direct way to invoke them from within SQL and application logic. These functions are implemented through the azure_ai extension, which brings model invocation directly into the Postgres engine. This allows developers to embed inference into queries and transactions, eliminating the need for external orchestration. By bringing model execution closer to the data, AI Functions reduce latency, simplify application design, and make intelligent behavior a natural extension of existing Postgres workloads. "What stood out with HorizonDB is that it aligns closely with how we already think about the problem. Instead of stitching together multiple components, it brings transactional data, vector search, and AI capabilities into a single platform, which simplifies the architecture without forcing a complete rethink." Mohsin Shafqat, Director Software Engineering, Nasdaq Run Reliable, Event-Driven Workflows with AI Pipelines Finally, AI Pipelines operationalize these capabilities through reliable, event-driven workflows for model execution and data processing. Pipelines execute on data changes, enabling real-time asynchronous reactions without external orchestration and ensuring consistent, repeatable behavior as data evolves. Combined with model management and AI Functions, they turn embedded intelligence into something that can be run, scaled, and trusted in production, while inheriting the database’s high availability and failover characteristics for resilience. Pipelines can also be visualized and observed in real time through the Visual Studio Code extension for PostgreSQL, giving developers and operators immediate visibility into execution flow, state, and outcomes Modern Unified Experience for Data, AI, and Operations in VS Code As intelligence becomes a core part of the data platform, the developer and operator experience becomes equally critical. HorizonDB extends seamlessly into Visual Studio Code with enhanced PostgreSQL tooling that works across any Postgres deployment, not just HorizonDB. Features like AI-assisted query plans and integrated monitoring enable faster debugging and optimization, helping teams understand both database performance and AI-driven behaviors. At the same time, for Azure-based deployments, the experience is deeply integrated with platform capabilities, enabling management of networking configuration, server parameters, and server logs directly from the development environment, streamlining operations across application and infrastructure layers. Azure HorizonDB brings together enterprise-grade security, deep Postgres compatibility, and a modern AI-native data platform, all engineered for developers. It scales efficiently across workloads, from transactional systems to intelligent applications, while delivering a world-class, Azure-integrated experience in Visual Studio Code for both developers and operators. Ready to get started with Azure HorizonDB? Azure HorizonDB is now available in public preview in Australia East, Central US, Sweden Central, West US 2, and West US 3 regions. Additionally, East US, Canada Central, Indonesia Central, Italy North, Japan East, Korea Central, and Poland Central will be available in the coming weeks. You can get started today by creating a new HorizonDB instance using the Azure portal, API’s, or the Visual Studio Code extension for PostgreSQL to begin exploring these capabilities firsthand. To learn more, dive deeper into our documentation and sign-up today to try AI model management in a limited preview.GitHub Copilot Dev Days Online
After a series of in-person events, GitHub Copilot Dev Days is now going online, bringing developers from around the world together to explore modern AI-assisted software development in practice. Through live sessions focused on agentic development, modern workflows, and hands-on learning in VS Code, attendees will learn how to use GitHub Copilot beyond autocomplete and apply it across real development scenarios. Register for the session that fits your language and community GitHub Copilot Dev Days LATAM [Spanish] - May 26 A hands-on session for Spanish-speaking developers across Latin America focused on building modern applications with GitHub Copilot, TypeScript, React, and Tailwind. Attendees will explore agentic workflows, context engineering, and practical ways to use GitHub Copilot as an active development partner in VS Code. Date: May 26, 2026, 12 PM (Mexico City / CDMX) Register: GitHub Copilot Dev Days LATAM | Microsoft Reactor GitHub Copilot Dev Days Brazil [Portuguese] - May 25 This edition focuses on AI-assisted development with Python, FastAPI, and HTMX using GitHub Copilot throughout the development workflow. The session covers practical workflows for code generation, refactoring, debugging, and day-to-day development with GitHub Copilot in VS Code. Date: May 25, 2026, 7 PM (Brasilia Time) Register: GitHub Copilot Dev Days Brasil | Microsoft Reactor GitHub Copilot Dev Days 中文版 [Simplified Chinese] - May 26 This session explores how GitHub Copilot and GitHub Actions can work together to create intelligent and automated development workflows. Topics include ChatOps, automated summaries, syncing content into GitHub Issues, and agentic workflows designed to improve collaboration and engineering efficiency. Date: May 26, 2026, 7:30 PM (China Standard Time - CST) Register: GitHub Copilot Dev Days - 中文版 | Microsoft Reactor GitHub Copilot Dev Days [English] - May 27 An English-language workshop for developers who want to learn how to build modern applications with GitHub Copilot in VS Code. The session focuses on TypeScript, React, Tailwind, and Agent Mode workflows, showing how better context and prompting can improve AI-assisted development. Date: May 27, 2026, 9 AM (PST) Register: GitHub Copilot Dev Days | Microsoft Reactor All sessions are hosted through Microsoft Reactor. Check the registration pages for local times and additional event details.Use AI to build AI, without losing your mind
with Maddy Montaquila, Lead PM for Aspire This is not just another AI discussion. This is a session for developers, architects, cloud engineers, and tech professionals who want to understand how AI can truly support modern software development, not create more confusion. We will explore how the right abstractions, strong defaults, and smart guardrails can help AI become a real accelerator for building applications. You will discover how agentic AI is changing the developer experience, how coding agents can help you move faster while staying in control, how Aspire supports building agentic applications, and how to avoid AI overload while staying focused on shipping real software. You will also learn how Microsoft Learn can support your continued journey in AI, cloud, and modern application development. 📢 Don’t miss this opportunity to learn, connect, and grow with the Microsoft Zero to Hero community. Register Here: https://streamyard.com/watch/5T8RNcRa6NUtAnnouncing Azure HorizonDB
Affan Dar, Vice President of Engineering, PostgreSQL at Microsoft Charles Feddersen, Partner Director of Program Management, PostgreSQL at Microsoft Today at Microsoft Ignite, we’re excited to unveil the preview of Azure HorizonDB, a fully managed Postgres-compatible database service designed to meet the needs of modern enterprise workloads. The cloud native architecture of Azure HorizonDB delivers highly scalable shared storage, elastic scale-out compute, and a tiered cache optimized for running cloud applications of any scale. Postgres is transforming industries worldwide and is emerging as the foundation of modern data solutions across all sectors at an unprecedented pace. For developers, it is the database of choice for building new applications with its rich set of extensions, open-source API, and expansive ecosystems of tools and libraries. At the same time, but at the opposite end of the workload spectrum, enterprises around the world are also increasingly turning to Postgres to modernize their existing applications. Azure HorizonDB is designed to support applications across the entire workload spectrum from the first line of code in a new app to the migration of large-scale, mission-critical solutions. Developers benefit from the robust Postgres ecosystem and seamless integration with Azure’s advanced AI capabilities, while enterprises can gain a secure, highly available, and performant cloud database to host their business applications. Whether you’re building from scratch or transforming legacy infrastructure, Azure HorizonDB empowers you to innovate and scale with confidence, today and into the future. Azure HorizonDB introduces new levels of performance and scalability to PostgreSQL. The scale-out compute architecture supports up to 3,072 vCores across primary and replica nodes, and the auto-scaling shared storage supports up to 128TB databases while providing sub-millisecond multi-zone commit latencies. This storage innovation enables Azure HorizonDB to deliver up to 3x more throughput when compared with open-source Postgres for transactional workloads. Azure HorizonDB is enterprise ready on day one. With native support for Entra ID, Private Endpoints, and data encryption, it provides compliance and security for sensitive data stored in the cloud. All data is replicated across availability zones by default and maintenance operations are transparent with near-zero downtime. Backups are fully automated, and integration with Azure Defender for Cloud provides additional protection for highly sensitive data. All up, Azure HorizonDB offers enterprise-grade security, compliance, and reliability, making it ready for business use today. Since the launch of ChatGPT, there has been an explosion of new AI apps being built, and Postgres has become the database of choice due in large part to its vector index support. Azure HorizonDB extends the AI capabilities of Postgres further with two key features. We are introducing advanced filtering capabilities to the DiskANN vector index which enable query predicate pushdowns directly into the vector similarity search. This provides significant performance and scalability improvements over pgvector HNSW while maintaining accuracy and is ideal for similarity search over transactional data in Postgres. The second feature is built-in AI model management that seamlessly integrates generative, embedding, and reranking models from Microsoft Foundry for developers to use in the database with zero configuration. In addition to enhanced vector indexing and simplified model management to build powerful new AI apps, we’re also pleased to announce the general availability of Microsoft’s PostgreSQL Extension for VS Code that provides the tooling for Postgres developers to maximize their productivity. Using this extension, GitHub Copilot is context aware of the Postgres database which means less prompting and higher quality answers, and in the Ignite release, we’ve added live monitoring with one-click GitHub Copilot debugging where Agent mode can launch directly from the performance monitoring dashboard to diagnose Postgres performance issues and guide users to a fix. Alpha Life Sciences are an existing Azure customers “I’m truly excited about how Azure HorizonDB empowers our AI development. Its seamless support for Vector DB, RAG, and Agentic AI allows us to build intelligent features directly on a reliable Postgres foundation. With Azure HorizonDB, I can focus on advancing AI capabilities instead of managing infrastructure complexities. It’s a smart, forward-looking solution that perfectly aligns with how we design and deliver AI-powered applications.” Pengcheng Xu, CTO Alpha Life Sciences For enterprises that are modernizing their applications to Postgres in the cloud, the security and availability of Azure HorizonDB make it an ideal platform. However, these migrations are often complex and time consuming for large legacy codebase conversions. To simplify this and reduce the risk, we’re pleased to announce the preview of GitHub Copilot powered Oracle migration built into the PostgreSQL Extension for VS Code. Built into VS Code, teams of engineers can work with GitHub Copilot to automate the end-to-end conversion of complex database code using rich code editing, version control, text authoring, and deployment in an integrated development environment. Azure HorizonDB is the next generation of fully managed, cloud native PostgreSQL database service. Built on the latest Azure infrastructure with state-of-the-art cloud architecture, Azure HorizonDB is ready to for the most demanding application workloads. In addition to our portfolio of managed Postgres services in Azure, Microsoft is deeply invested into the open source Postgres project and is one of the top corporate upstream contributors and sponsors for the PostgreSQL project, with 19 Postgres project contributors employed by Microsoft. As a hyperscale Postgres vendor, it’s critical to actively participate in the open-source project. It enables us to better support our customers down to the metal in Azure, and to contribute our learnings from running Postgres at scale back to the community. We’re committed to continuing our investment to push the Postgres project forward, and the team is already active in making contributions to Postgres 19 to be released in 2026. Ready to explore Azure HorizonDB? Azure HorizonDB is initially available in Central US, West US3, UK South and Australia East regions. Customers are invited to apply for early preview access to Azure HorizonDB and get hands-on experience with this new service. Participation is limited, apply now at aka.ms/PreviewHorizonDBEdge AI for Beginners : Getting Started with Foundry Local
In Module 08 of the EdgeAI for Beginners course, Microsoft introduces Foundry Local a toolkit that helps you deploy and test Small Language Models (SLMs) completely offline. In this blog, I’ll share how I installed Foundry Local, ran the Phi-3.5-mini model on my windows laptop, and what I learned through the process. What Is Foundry Local? Foundry Local allows developers to run AI models locally on their own hardware. It supports text generation, summarization, and code completion — all without sending data to the cloud. Unlike cloud-based systems, everything happens on your computer, so your data never leaves your device. Prerequisites Before starting, make sure you have: Windows 10 or 11 Python 3.10 or newer Git Internet connection (for the first-time model download) Foundry Local installed Step 1 — Verify Installation After installing Foundry Local, open Command Prompt and type: foundry --version If you see a version number, Foundry Local is installed correctly. Step 2 — Start the Service Start the Foundry Local service using: foundry service start You should see a confirmation message that the service is running. Step 3 — List Available Models To view the models supported by your system, run: foundry model list You’ll get a list of locally available SLMs. Here’s what I saw on my machine: Note: Model availability depends on your device’s hardware. For most laptops, phi-3.5-mini works smoothly on CPU. Step 4 — Run the Phi-3.5 Model Now let’s start chatting with the model: foundry model run phi-3.5-mini-instruct-generic-cpu:1 Once it loads, you’ll enter an interactive chat mode. Try a simple prompt: Hello! What can you do? The model replies instantly — right from your laptop, no cloud needed. To exit, type: /exit How It Works Foundry Local loads the model weights from your device and performs inference locally.This means text generation happens using your CPU (or GPU, if available). The result: complete privacy, no internet dependency, and instant responses. Benefits for Students For students beginning their journey in AI, Foundry Local offers several key advantages: No need for high-end GPUs or expensive cloud subscriptions. Easy setup for experimenting with multiple models. Perfect for class assignments, AI workshops, and offline learning sessions. Promotes a deeper understanding of model behavior by allowing step-by-step local interaction. These factors make Foundry Local a practical choice for learning environments, especially in universities and research institutions where accessibility and affordability are important. Why Use Foundry Local Running models locally offers several practical benefits compared to using AI Foundry in the cloud. With Foundry Local, you do not need an internet connection, and all computations happen on your personal machine. This makes it faster for small models and more private since your data never leaves your device. In contrast, AI Foundry runs entirely on the cloud, requiring internet access and charging based on usage. For students and developers, Foundry Local is ideal for quick experiments, offline testing, and understanding how models behave in real-time. On the other hand, AI Foundry is better suited for large-scale or production-level scenarios where models need to be deployed at scale. In summary, Foundry Local provides a flexible and affordable environment for hands-on learning, especially when working with smaller models such as Phi-3, Qwen2.5, or TinyLlama. It allows you to experiment freely, learn efficiently, and better understand the fundamentals of Edge AI development. Optional: Restart Later Next time you open your laptop, you don’t have to reinstall anything. Just run these two commands again: foundry service start foundry model run phi-3.5-mini-instruct-generic-cpu:1 What I Learned Following the EdgeAI for Beginners Study Guide helped me understand: How edge AI applications work How small models like Phi 3.5 can run on a local machine How to test prompts and build chat apps with zero cloud usage Conclusion Running the Phi-3.5-mini model locally with Foundry Localgave me hands-on insight into edge AI. It’s an easy, private, and cost-free way to explore generative AI development. If you’re new to Edge AI, start with the EdgeAI for Beginners course and follow its Study Guide to get comfortable with local inference and small language models. Resources: EdgeAI for Beginners GitHub Repo Foundry Local Official Site Phi Model LinkModel Mondays S2E10: Automating Document Processing with AI
1. Weekly Highlights We kicked off with the top news and updates in the Azure AI ecosystem: Agent Factory Blog Series: A new 6-part blog series on designing reliable, agentic AI—exploring multi-step, collaborative agents that reflect, plan, and adapt using tool integrations and design patterns. Text PII Preview in Azure AI Language: Now redacts PII (like date of birth, license plates) in major European languages, with better accuracy for UK bank entities. Claude Opus 4.1 in Copilot Pro & Enterprise: Public preview brings smarter summaries, tool assistant thinking, and "Ask Mode" in VS Code.Now leverages stronger computer vision algorithms for table parsing—achieving 94-97% accuracy across Latin, Chinese, Japanese, and Korean—with sub-10ms latency. Mistral Document AI in Azure Foundry: Instantly turn PDFs, contracts, and scanned docs into structured JSON with tables, headings, and LaTeX support. Serverless, multilingual, secure, and perfect for regulated industries. 2. Spotlight On: Document Intelligence with Azure & Mistral This week’s spotlight was a hands-on exploration of document processing, featuring both Microsoft and Mistral AI experts. Why Document Processing? Unstructured data—receipts, forms, handwritten notes—are everywhere. Modern document AI can extract, structure, and even annotate this data, fueling everything from search to RAG pipelines. Azure Document Intelligence: State-of-the-art OCR and table extraction with super-high accuracy and speed. Handles multi-language, complex layouts, and returns structured outputs ready for programmatic use. Mistral Document AI: Transforms PDFs and scanned docs into JSON, retaining complex formatting, tables, images, and even LaTeX. Supports custom schema extraction, image/document annotations, and returns everything in one API call. Integrates seamlessly with Azure AI Foundry and developer workflows. Demo Highlights: Extracting Receipts: OCR accurately pulls out store, date, and transaction details from photos. Handwriting Recognition: Even historical documents (like Thomas Jefferson’s letters) are parsed with surprising accuracy. Tables & Structured Data: Financial statements and reports converted into structured markdown and JSON—ready for downstream apps. Advanced Annotations: Define your own schema (via JSON Schema or Pydantic), extract custom fields, classify images, summarize documents, and even translate summaries—all in a single call. 3. Customer Story: Oracle Health Oracle Health shared how agentic AI and fine-tuned models are revolutionizing clinical workflows: Problem: Clinicians spend hours on documentation, searching records, and manual data entry—reducing time for patient care. Solution: Oracle’s clinical AI agents automate chart reviews, data extraction, and even conversational Q&A—while keeping humans in the loop for safety. Technical Highlights: Multi-agent architecture understands provider specialty and context. Orchestrator model "routes" requests to the right agent or plugin, extracting needed arguments from context. Fine-tuning was key: For low latency, Oracle used lightweight models (like GPT-4 Mini) and fine-tuned on their data—achieving sub-800ms responses, with accuracy matching larger models. Fine-tuning also allowed for nuanced tool selection, argument extraction, and rule-based orchestration—better than prompt engineering alone. Used LoRA for efficient, targeted fine-tuning without erasing base model knowledge. Live Demo: Agent summarizes patient history, retrieves lab results, filters for abnormals, and answers follow-up questions—all conversationally. Fine-tuned orchestrator chooses the right tool and context for each doctor’s workflow. Result: 1-2 hours saved per day, more time for patients, and happier doctors! 4. Key Takeaways Here are the key learnings from this episode: Document AI is Production-Ready: Azure Document Intelligence and Mistral Document AI offer fast, accurate, and customizable document parsing for real enterprise needs. Schema-Driven Extraction & Annotation: Define your own schemas and extract exactly what you want—no more one-size-fits-all. Fine-Tuning Unlocks Performance: For low latency and high accuracy, fine-tuning lightweight models beats prompt engineering in complex, rule-based agent workflows. Agentic Workflows in Action: Multi-agent systems can automate complex tasks, route requests, and keep humans in control, especially in regulated domains like healthcare. Community & Support: Join the Discord and Forum to ask questions, share use cases, and connect with the team. Sharda's Tips: How I Wrote This Blog Writing this recap is all about sharing what I learned and making it practical for the community! I start by organizing the key highlights, then walk through customer stories and demos, using simple language and real-world examples. Copilot helps me structure and clarify my notes, especially when summarizing technical sections. Here’s the prompt I used for Copilot this week: "Generate a technical blog post for Model Mondays S2E10 based on the transcript and episode details. Focus on document processing with Azure AI and Mistral, include customer demos, and highlight practical workflows and fine-tuning. Make it clear and approachable for developers and students." Every episode inspires me to try these tools myself, and I hope this blog makes it easy for you to start, too. If you have questions or want to share your own experience, I’d love to hear from you! Coming Up Next Week Next week: Text & Speech AI Playgrounds! Learn how to build and test language and speech models, with live demos and expert guests. | Register For The Livestream – Aug 25, 2025 | Register For The AMA – Aug 29, 2025 | Ask Questions & View Recaps – Discussion Forum About Model Mondays Model Mondays is a weekly series to build your Azure AI IQ with: 5-Minute Highlights: News & updates on Mondays 15-Minute Spotlight: Deep dives into new features, models, and protocols 30-Minute AMA Fridays: Live Q&A with product teams and experts Get started: Register For Livestreams Watch Past Replays Register For AMA Recap Past AMAs Join The Community Don’t build alone! Join the Azure AI Developer Community for real-time chats, events, support, and more: Join the Discord Explore the Forum About Me I'm Sharda, a Gold Microsoft Learn Student Ambassador focused on cloud and AI. Find me on GitHub, Dev.to, Tech Community, and LinkedIn. In this blog series, I share takeaways from each week’s Model Mondays livestream.
