Getting Started with AI and MS Copilot - French
Souhaitez-vous découvrir l’intelligence artificielle (IA) et Microsoft Copilot de manière pratique et ludique ? Nous vous invitons à participer à la séance intitulée « Introduction à l’IA et Microsoft Copilot », spécialement conçue pour les membres du corps enseignant qui débutent avec Microsoft Copilot. Cette séance vous permettra d’acquérir les notions fondamentales de l’IA générative, de comprendre comment formuler des requêtes efficaces (invites, ou « prompts ») et d’explorer comment appliquer ces outils en classe. Vous aurez accès à des supports pédagogiques que vous pourrez utiliser en classe et vous aurez l’occasion de mettre vos connaissances en pratique à travers 10 exercices. Rejoignez la réunion iciGetting Started with AI and MS Copilot - French
Souhaitez-vous découvrir l’intelligence artificielle (IA) et Microsoft Copilot de manière pratique et ludique ? Nous vous invitons à participer à la séance intitulée « Introduction à l’IA et Microsoft Copilot », spécialement conçue pour les membres du corps enseignant qui débutent avec Microsoft Copilot. Cette séance vous permettra d’acquérir les notions fondamentales de l’IA générative, de comprendre comment formuler des requêtes efficaces (invites, ou « prompts ») et d’explorer comment appliquer ces outils en classe. Vous aurez accès à des supports pédagogiques que vous pourrez utiliser en classe et vous aurez l’occasion de mettre vos connaissances en pratique à travers 10 exercices. Rejoignez la réunion iciPower Up Your Open WebUI with Azure AI Speech: Quick STT & TTS Integration
Introduction Ever found yourself wishing your web interface could really talk and listen back to you? With a few clicks (and a bit of code), you can turn your plain Open WebUI into a full-on voice assistant. In this post, you’ll see how to spin up an Azure Speech resource, hook it into your frontend, and watch as user speech transforms into text and your app’s responses leap off the screen in a human-like voice. By the end of this guide, you’ll have a voice-enabled web UI that actually converses with users, opening the door to hands-free controls, better accessibility, and a genuinely richer user experience. Ready to make your web app speak? Let’s dive in. Why Azure AI Speech? We use Azure AI Speech service in Open Web UI to enable voice interactions directly within web applications. This allows users to: Speak commands or input instead of typing, making the interface more accessible and user-friendly. Hear responses or information read aloud, which improves usability for people with visual impairments or those who prefer audio. Provide a more natural and hands-free experience especially on devices like smartphones or tablets. In short, integrating Azure AI Speech service into Open Web UI helps make web apps smarter, more interactive, and easier to use by adding speech recognition and voice output features. If you haven’t hosted Open WebUI already, follow my other step-by-step guide to host Ollama WebUI on Azure. Proceed to the next step if you have Open WebUI deployed already. Learn More about OpenWeb UI here. Deploy Azure AI Speech service in Azure. Navigate to the Azure Portal and search for Azure AI Speech on the Azure portal search bar. Create a new Speech Service by filling up the fields in the resource creation page. Click on “Create” to finalize the setup. After the resource has been deployed, click on “View resource” button and you should be redirected to the Azure AI Speech service page. The page should display the API Keys and Endpoints for Azure AI Speech services, which you can use in Open Web UI. Settings things up in Open Web UI Speech to Text settings (STT) Head to the Open Web UI Admin page > Settings > Audio. Paste the API Key obtained from the Azure AI Speech service page into the API key field below. Unless you use different Azure Region, or want to change the default configurations for the STT settings, leave all settings to blank. Text to Speech settings (TTS) Now, let's proceed with configuring the TTS Settings on OpenWeb UI by toggling the TTS Engine to Azure AI Speech option. Again, paste the API Key obtained from Azure AI Speech service page and leave all settings to blank. You can change the TTS Voice from the dropdown selection in the TTS settings as depicted in the image below: Click Save to reflect the change. Expected Result Now, let’s test if everything works well. Open a new chat / temporary chat on Open Web UI and click on the Call / Record button. The STT Engine (Azure AI Speech) should identify your voice and provide a response based on the voice input. To test the TTS feature, click on the Read Aloud (Speaker Icon) under any response from Open Web UI. The TTS Engine should reflect Azure AI Speech service! Conclusion And that’s a wrap! You’ve just given your Open WebUI the gift of capturing user speech, turning it into text, and then talking right back with Azure’s neural voices. Along the way you saw how easy it is to spin up a Speech resource in the Azure portal, wire up real-time transcription in the browser, and pipe responses through the TTS engine. From here, it’s all about experimentation. Try swapping in different neural voices or dialing in new languages. Tweak how you start and stop listening, play with silence detection, or add custom pronunciation tweaks for those tricky product names. Before you know it, your interface will feel less like a web page and more like a conversation partner.Announcing the new Microsoft Learn Plan - Preparing for your organization's AI workloads
We're pleased to announce the new "Preparing for your organization's AI workloads" Microsoft Learn Plan - focused the IT/Ops audience and now available on Microsoft Learn! This set of content was curated by our team and is targeted at helping IT Professionals who want to learn how to support their organization's AI applications and infrastructure. The Learning Plan is composed of 4 milestones, which in its turn are composed of a total of 22 modules: Milestone 1: Getting Started with AI on Microsoft Azure - Learning Path: Introduction to AI in Azure - 12 modules Milestone 2: Introduction to AI Services Infrastructure on Azure - Learning Path: Manage Authentication, Authorization, and RBAC for AI Workloads on Azure - 3 modules - Learning Path: Manage Network Access for AI Workloads - 2 modules Milestone 3: Monitoring AI Services on Azure - Learning Path: Monitor AI Workloads on Azure - 3 modules Milestone 4: Advanced Management of AI Workloads on Azure - Learning Path: AI Workload Governance and DLP - 2 modules This comprehensive plan introduces foundational AI concepts, then guides you through advanced topics. Whether you're an IT administrator, security specialist, or AI practitioner, this plan equips you with the skills to build trusted, secure, and compliant AI solutions at scale. We hope you enjoy learning! Let us know what you think about this content in the comment section below! If you'd like to see more of this type of content, or have any suggestions, let us know as well!AZ-500: Microsoft Azure Security Technologies Study Guide
The AZ-500 certification provides professionals with the skills and knowledge needed to secure Azure infrastructure, services, and data. The exam covers identity and access management, data protection, platform security, and governance in Azure. Learners can prepare for the exam with Microsoft's self-paced curriculum, instructor-led course, and documentation. The certification measures the learner’s knowledge of managing, monitoring, and implementing security for resources in Azure, multi-cloud, and hybrid environments. Azure Firewall, Key Vault, and Azure Active Directory are some of the topics covered in the exam.Getting Started with AI and MS Copilot — French
Souhaitez-vous découvrir l’intelligence artificielle (IA) et Microsoft Copilot de manière pratique et ludique ? Nous vous invitons à participer à la séance intitulée « Introduction à l’IA et Microsoft Copilot », spécialement conçue pour les membres du corps enseignant qui débutent avec Microsoft Copilot. Cette séance vous permettra d’acquérir les notions fondamentales de l’IA générative, de comprendre comment formuler des requêtes efficaces (invites, ou « prompts ») et d’explorer comment appliquer ces outils en classe. Vous aurez accès à des supports pédagogiques que vous pourrez utiliser en classe et vous aurez l’occasion de mettre vos connaissances en pratique à travers 10 exercices. Rejoignez la réunion iciTrain a simple Recommendation Engine using the new Azure AI Studio
The AI Studio Odyssey: Embark on a journey to the heart of personalization with our latest guide, “Train a Simple Recommendation Engine using the new Azure AI Studio.” Unlock the secrets of the all-new Azure AI Studio intuitive tools to craft a recommendation system that feels like magic, yet is grounded in data and user preferences. Ready to enchant your audience? Grab some popcorn and read on!Utilizando un archivo en GitHub Copilot para Visual Studio
Cuando creas un nuevo proyecto desde cero en Visual Studio, algunos archivos se crean. Hay muchas plantillas disponibles, para muchos tipos de aplicaciones, desde aplicaciones simples hasta aplicaciones web complejas, así como aplicaciones móviles, sin servidor y muchas más. Todos estos proyectos constan de varios archivos. Tienes tus archivos de código, que contienen el software que se ejecutará, organizados en clases, frecuentemente cada clase en su propio archivo. Tienes los archivos de configuración, ya sea JSON, XML, YAML u otros. Incluso puedes tener archivos de datos, incrustados en la aplicación cuando está construida. En un video que se publicó, mi compañera Gwyn muestra cómo puedes usar el atajo Hash (#) para hacer referencia a otro archivo. [Este post es una traducción del blog original escrito en inglés por Laurent Bugnion y Gwyn Peña-Sigüenza] El contexto lo es todo Como mencionamos en varias ocasiones, lo que hace que una respuesta de GitHub Copilot sea buena comienza con un buen prompt. Sin embargo, el prompt no es solo pedirle al modelo de lenguaje que haga algo; también es necesario proporcionar contexto. En el mundo de la IA, nos referimos a esto como 'grounding' del modelo con datos, o Generación Aumentada por Recuperación (RAG). A través de su entrenamiento, Copilot tiene acceso a conocimientos generales sobre la plataforma que estás utilizando, así como a conocimientos específicos sobre bibliotecas y frameworks. Sin embargo, lo que falta es tu propio código privado, el código que el resto del mundo no ve. Por ejemplo, puedes informar a GitHub Copilot que otro archivo contiene una serie de métodos que la clase actual puede utilizar. En el ejemplo, Gwyn le indica a GitHub Copilot un archivo JSON que contiene datos para generar una prueba. Esto añade un contexto valioso, permitiendo que Copilot genere el código correcto de manera más rápida. Más información Como siempre, puedes encontrar muchos recursos educativos gratuitos en esta colección de Microsoft Learn. Y, por supuesto, la mejor manera de estar al día es suscribirse al canal de YouTube de Visual Studio, al Visual Studio DevBlog y, por supuesto, a este blog.Introducing Microsoft Planetary Computer Pro — Now in Public Preview
Today, we’re excited to announce the public preview of Microsoft Planetary Computer Pro — a turnkey platform that makes it dramatically easier for organizations to harness geospatial data for real-world impact. Planetary Computer Pro is built on the trusted foundation of Microsoft Planetary Computer, which offers access to over 120 distinct geospatial datasets totaling over 50PB in volume. Planetary Computer Pro is a new Azure-native service purpose-built to help organizations manage, transform, and operationalize geospatial data at enterprise scale. Geospatial data and insights are critical for solving high-impact problems across industries, from climate risk assessment and regulatory compliance to supply chain optimization and precision agriculture. Yet, traditional geospatial tooling is complex and fragmented, limiting access to a small group of geospatial specialists. Planetary Computer Pro bridges that gap — making geospatial data cloud-native, AI-ready, and accessible to data scientists, developers, and business analysts alike. We built Microsoft Planetary Computer Pro to make geospatial data a first-class citizen in modern data stacks — standardized, scalable, and seamlessly integrated with the tools enterprises already use. Geospatial Data Management, Reimagined Planetary Computer Pro is a fully managed geospatial data platform designed to ingest, catalog, store, process, and disseminate large volumes of private geospatial data in Azure. Planetary Computer Pro makes it possible to: Empower your entire organization with secure, governed access to geospatial data Accelerate time-to-insights with built-in ingestion, transformation, and visualization pipelines Standardize and optimize your datasets for cloud-native analytics, machine learning and AI modeling Unify geospatial and enterprise systems under shared security, identity, and governance Key Capabilities in Public Preview You can deploy, manage, and monitor Planetary Computer Pro resources through Azure Portal, CLI, or SDKs, just like any other Azure-native resource provider. Capability Description Cloud Optimization Auto-convert raw geospatial assets into cloud-optimized formats with built-in ingestion pipeline for AI/ML and big data analytics Data Interoperability Organize multiple datasets into SpatioTemporal Asset Catalog (STAC) open specification, allowing for robust spatial/temporal queryability and interoperability Managed Storage & APIs Fully managed storage and interact with data using intuitive REST APIs (API Reference Guide) Rich Visualization Explore and analyze large datasets in a web-based Data Explorer, including tiling and mosaic rendering for raster data and data cube formats (Supported Data Types) Scalability & Security Built on zone-redundant storage, governed by Microsoft Entra ID and Azure RBAC Use Cases Across Industries Microsoft Planetary Computer Pro supports a broad spectrum of scenarios across sectors such as: Energy & Utilities: Power grid optimization, site monitoring, methane detection Agriculture: Precision farming, pest & disease prediction Supply Chain: Risk-aware routing, climate-resilient sourcing Finance & Insurance: Underwriting, claims validation, exposure modeling Government: Emergency response, environmental monitoring, land use compliance Defense & Intelligence: ISR, threat detection, terrain analysis Sustainability Teams: Deforestation mapping, EUDR compliance, biodiversity tracking Get Started with Public Preview The public preview of Microsoft Planetary Computer Pro is available now in select Azure regions including East US, North Central US, and West Europe. To get started: Visit Microsoft Planetary Computer Pro Review our documentation Microsoft Planetary Computer Pro | Microsoft Learn Contact us at MPCPro@microsoft.com What’s Next? We’re actively working on: Platform Integration: Expanded integration with Microsoft Fabric Direct access to Microsoft Planetary Computer Commercial satellite imagery access via Azure Marketplace AI and Automation: Automated raster data workflow environment Copilot and agent-assisted insights generation Platform Enhancement: Additional geospatial data type support New region availability and government cloud supportAI Agents in Production: From Prototype to Reality - Part 10
This blog post, the tenth and final installment in a series on AI agents, focuses on deploying AI agents to production. It covers evaluating agent performance, addressing common issues, and managing costs. The post emphasizes the importance of a robust evaluation system, providing potential solutions for performance issues, and outlining cost management strategies such as response caching, using smaller models, and implementing router models.
