Enhancing Infrastructure as Code Generation with GitHub Copilot for Azure
Discover the latest update to GitHub Copilot for Azure, designed to streamline Infrastructure as Code (IaC) generation using Bicep or Terraform. With a new update panel, developers can easily modify project details, hosting services, target services, bindings, and environment variablesโall within an intuitive UI. This enhancement eliminates the need for chat-based modifications, improving efficiency and reducing errors. Save time, automate infrastructure deployment, and experience seamless cloud configuration. Try the new GitHub Copilot for Azure update today and optimize your Azure development workflow effortlessly!Get certified as an Azure AI Engineer (AI-102) this summer?
For developers, the accreditation as an Azure AI Engineerโcertified through the rigorous AI-102 examโhas become a golden ticket to career acceleration. It isnโt just about coding chatbots or fine-tuning machine learning models; itโs about gaining the confidence (for you and for your business) that you can wield Azureโs toolkits to configure AI solutions that augment human capability. Before we dive in, if youโre planning to become certified as an Azure AI Engineer, you may find this Starter Learning Plan (AI 102) valuableโrecently curated by a group of Microsoft experts, purposed for your success. We recommend adding it to your existing learning portfolio. Itโs a light introduction that should take less than four hours, but it offers a solid glimpse into what to expect on your journey and the breadth of solutions you might craft in the future. From revolutionizing customer service with intelligent agents to optimizing supply chains through predictive analytics, Azure AI engineers sit at the confluence of technological ingenuity and business transformation. For those with an appetite for problem-solving and a vision for AI-driven futures, this certification isnโt just another badgeโitโs an assertion of expertise in a field where demand is outpacing supply. Securing that expertise, however, requires more than just a weekend of cramming. Todayโs aspiring AI engineers navigate an ecosystem of learning that is as modern as the field itself. Gone are the days when one could rely solely on a stack of manuals; now, candidates immerse themselves in a medley of Microsoft Learn modules, hands-on labs, AI-powered coding assistants, and community-led study groups. Many take a pragmatic approachโbuilding real-world projects using Azure Cognitive Services and Machine Learning Studio to cement their understanding. Others lean on practice exams and structured courses from platforms like Pluralsight and Udemy, ensuring they arenโt just memorizing but internalizing the core principles. The AI-102 exam doesnโt reward rote knowledgeโit demands fluency in designing, deploying, and securing AI solutions, making thorough preparation an indispensable part of the journey. In addition to the above learning plan, we want to provide a few other tips. Understand the Exam Objectives: Begin by thoroughly reviewing the AI-102 study guide. This document outlines the key topics and skills assessed, including planning and managing Azure AI solutions, implementing computer vision and natural language processing solutions, and deploying generative AI solutions. Familiarizing yourself with these areas will provide a structured framework for your study plan. Continuous memorization is part of your study. But if you get a bit bored from your flashcards and look for more โstorylineโ style learning content, we recommend adding MSFT employee created learning plan to your mix. They are scenario-based and focus more on providing you with a structured understanding of how to do XYZ on Azure. Here are 3 examples: Modernize for AI Readiness Build AI apps with Azure Re-platform AI applications Hands-On Practice: Practical experience is invaluable. Engage with Azure AI services directly by building projects that incorporate computer vision, natural language processing, and other AI functionalities. This hands-on approach not only reinforces theoretical knowledge but also enhances problem-solving skills in real-world scenarios. Utilize Practice Assessments: Assess your readiness by taking advantage of free practice assessments provided by Microsoft. These assessments mirror the style and difficulty of actual exam questions, offering detailed feedback and links to additional resources for areas that may require further study. Stay Updated on Exam Changes: Certification exams are periodically updated to reflect the latest technologies and practices. Regularly consult the official exam page to stay informed about any changes in exam content or structure. Participate in Community Discussions: Engaging with peers through forums and study groups can provide diverse perspectives and insights. The Microsoft Q&A platform is a valuable resource for asking questions, sharing knowledge, and learning from the experiences of others preparing for the same certification. By systematically incorporating these strategies into your preparation, you'll be well-positioned to excel in the AI-102 exam and advance your career as an Azure AI Engineer. If you have additional tips or thoughts, let us know in the comments area. Good luck!Use AI for Free with GitHub Models and TypeScript! ๐ธ๐ธ๐ธ
Learn how to use AI for free with GitHub Models! Test models like GPT-4o without paying for APIs or setting up infrastructure. This step-by-step guide shows how to integrate GitHub Models with TypeScript in the Microblog AI Remix project. Start exploring AI for free today!Speed Up OpenAI Embedding By 4x With This Simple Trick!
In todayโs fast-paced world of AI applications, optimizing performance should be one of your top priorities. This guide walks you through a simple yet powerful way to reduce OpenAI embedding response sizes by 75%โcutting them from 32 KB to just 8 KB per request. By switching from float32 to base64 encoding in your Retrieval-Augmented Generation (RAG) system, you can achieve a 4x efficiency boost, minimizing network overhead, saving costs and dramatically improving responsiveness. Let's consider the following scenario. Use Case: RAG Application Processing a 10-Page PDF A user interacts with a RAG-powered application that processes a 10-page PDF and uses OpenAI embedding models to make the document searchable from an LLM. The goal is to show how optimizing embedding response size impacts overall system performance. Step 1: Embedding Creation from the 10-Page PDF In a typical RAG system, the first step is to embed documents (in this case, a 10-page PDF) to store meaningful vectors that will later be retrieved for answering queries. The PDF is split into chunks. In our example, each chunk contains approximately 100 tokens (for the sake of simplicity), but the recommended chunk size varies based on the language and the embedding model. Assumptions for the PDF: - A 10-page PDF has approximately 3325 tokens (about 300 tokens per page). - Youโll split this document into 34 chunks (each containing 100 tokens). - Each chunk will then be sent to the embedding OpenAI API for processing. Step 2: The User Interacts with the RAG Application Once the embeddings for the PDF are created, the user interacts with the RAG application, querying it multiple times. Each query is processed by retrieving the most relevant pieces of the document using the previously created embeddings. For simplicity, letโs assume: - The user sends 10 queries, each containing 200 tokens. - Each query requires 2 embedding requests (since the query is split into 100-token chunks for embedding). - After embedding the query, the system performs retrieval and returns the most relevant documents (the RAG response). Embedding Response Size The OpenAI Embeddings models take an input of tokens (the text to embed) and return a list of numbers called a vector. This list of numbers represents the โembeddingโ of the input in the model so that it can be compared with another vector to measure similarity. In RAG, we use embedding models to quickly search for relevant data in a vector database. By default, embeddings are serialized as an array of floating-point values in a JSON document so each response from the embedding API is relatively large. The array values are 32-bit floating point numbers, or float32. Each float32 value occupies 4 bytes, and the embedding vector returned by models like OpenAIโs text-embedding-ada-002 typically consists of 1536-dimensional vectors. The challenge is the size of the embedding response: - Each response consists of 1536 float32 values (one per dimension). - 1536 float32 values result in 6144 bytes (1536 ร 4 bytes). - When serialized as UTF-8 for transmission over the network, this results in approximately 32 KB per response due to additional serialization overhead (like delimiters). Optimizing Embedding Response Size One approach to optimize the embedding response size is to serialize the embedding as base64. This encoding reduces the overall size by compressing the data, while maintaining the integrity of the embedding information. This leads to a significant reduction in the size of the embedding response. With base64-encoded embeddings, the response size reduces from 32 KB to approximately 8 KB, as demonstrated below: base64 vs float32 Min (Bytes) Max (Bytes) Mean (Bytes) Min (+) Max (+) Mean (+) 100 tokens embeddings: text-embedding-3-small 32673.000 32751.000 32703.800 8192.000 (4.0x) (74.9%) 8192.000 (4.0x) (75.0%) 8192.000 (4.0x) (74.9%) 100 tokens embeddings: text-embedding-3-large 65757.000 65893.000 65810.200 16384.000 (4.0x) (75.1%) 16384.000 (4.0x) (75.1%) 16384.000 (4.0x) (75.1%) 100 tokens embeddings: text-embedding-ada-002 32882.000 32939.000 32909.000 8192.000 (4.0x) (75.1%) 8192.000 (4.0x) (75.2%) 8192.000 (4.0x) (75.1%) The source code of this benchmark can be found at: https://github.com/manekinekko/rich-bench-node (kudos to Anthony Shaw for creating the rich-bench python runner) Comparing the Two Scenarios Letโs break down and compare the total performance of the system in two scenarios: Scenario 1: Embeddings Serialized as float32 (32 KB per Response) Scenario 2: Embeddings Serialized as base64 (8 KB per Response) Scenario 1: Embeddings Serialized as Float32 In this scenario, the PDF embedding creation and user queries involve larger responses due to float32 serialization. Letโs compute the total response size for each phase: 1. Embedding Creation for the PDF: - 34 embedding requests (one per 100-token chunk). - 34 responses with 32 KB each. Total size for PDF embedding responses: 34 ร 32 KB = 1088 KB = 1.088 MB 2. User Interactions with the RAG App: - Each user query consists of 200 tokens (which is split into 2 chunks of 100 tokens). - 10 user queries, requiring 2 embedding responses per query (for 2 chunks). - Each embedding response is 32 KB. Total size for user queries: Embedding responses: 20 ร 32 KB = 640 KB. RAG responses: 10 ร 32 KB = 320 KB. Total size for user interactions: 640 KB (embedding) + 320 KB (RAG) = 960 KB. 3. Total Size: Total size for embedding responses (PDF + user queries): 1088 KB + 640 KB = 1.728 MB Total size for RAG responses: 320 KB. Overall total size for all 10 responses: 1728 KB + 320 KB = 2048 KB = 2 MB Scenario 2: Embeddings Serialized as Base64 In this optimized scenario, the embedding response size is reduced to 8 KB by using base64 encoding. 1. Embedding Creation for the PDF: - 34 embedding requests. - 34 responses with 8 KB each. Total size for PDF embedding responses: 34 ร 8 KB = 272 KB. 2. User Interactions with the RAG App: - Embedding responses for 10 queries, 2 responses per query. - Each embedding response is 8 KB. Total size for user queries: Embedding responses: 20 ร 8 KB = 160 KB. RAG responses: 10 ร 8 KB = 80 KB. Total size for user interactions: 160 KB (embedding) + 80 KB (RAG) = 240 KB 3. Total Size (Optimized Scenario): Total size for embedding responses (PDF + user queries): 272 KB + 160 KB = 432 KB. Total size for RAG responses: 80 KB. Overall total size for all 10 responses: 432 KB + 80 KB = 512 KB Performance Gain: Comparison Between Scenarios The optimized scenario (base64 encoding) is 4 times smaller than the original (float32 encoding): 2048 / 512 = 4 times smaller. The total size reduction between the two scenarios is: 2048 KB - 512 KB = 1536 KB = 1.536 MB. And the reduction in data size is: (1536 / 2048) ร 100 = 75% reduction. How to Configure base64 encoding format When getting a vector representation of a given input that can be easily consumed by machine learning models and algorithms, as a developer, you usually call either the OpenAI API endpoint directly or use one of the official libraries for your programming language. Calling the OpenAI or Azure OpenAI APIs Using OpenAI endpoint: curl -X POST "https://api.openai.com/v1/embeddings" \ -H "Content-Type: application/json" \ -H "Authorization: Bearer YOUR_API_KEY" \ -d '{ "input": "The five boxing wizards jump quickly", "model": "text-embedding-ada-002", "encoding_format": "base64" }' Or, calling Azure OpenAI resources: curl -X POST "https://{endpoint}/openai/deployments/{deployment-id}/embeddings?api-version=2024-10-21" \ -H "Content-Type: application/json" \ -H "api-key: YOUR_API_KEY" \ -d '{ "input": ["The five boxing wizards jump quickly"], "encoding_format": "base64" }' Using OpenAI Libraries JavaScript/TypeScript const response = await client.embeddings.create({ input: "The five boxing wizards jump quickly", model: "text-embedding-3-small", encoding_format: "base64" }); A pull request has been sent to the openai SDK for Node.js repository to make base64 the default encoding when/if the user does not provide an encoding. Please feel free to give that PR a thumb up. Python embedding = client.embeddings.create( input="The five boxing wizards jump quickly", model="text-embedding-3-small", encoding_format="base64" ) NB: from 1.62 the openai SDK for Python will default to base64. Java EmbeddingCreateParams embeddingCreateParams = EmbeddingCreateParams .builder() .input("The five boxing wizards jump quickly") .encodingFormat(EncodingFormat.BASE64) .model("text-embedding-3-small") .build(); .NET The openai-dotnet library is already enforcing the base64 encoding, and does not allow setting encoding_format by the user (see). Conclusion By optimizing the embedding response serialization from float32 to base64, you achieved a 75% reduction in data size and improved performance by 4x. This reduction significantly enhances the efficiency of your RAG application, especially when processing large documents like PDFs and handling multiple user queries. For 1 million users sending 1,000 requests per month, the total size saved would be approximately 22.9 TB per month simply by using base64 encoded embeddings. As demonstrated, optimizing the size of the API responses is not only crucial for reducing network overhead but also for improving the overall responsiveness of your application. In a world where efficiency and scalability are key to delivering robust AI-powered solutions, this optimization can make a substantial difference in both performance and user experience. โ Shoutout to my colleague Anthony Shaw for the the long and great discussions we had about embedding optimisations.Unlocking the Power of Azure Container Apps in 1 Minute Video
Azure Container Apps provides a seamless way to build, deploy, and scale cloud-native applications without the complexity of managing infrastructure. Whether youโre developing microservices, APIs, or AI-powered applications, this fully managed service enables you to focus on writing code while Azure handles scalability, networking, and deployments. In this blog post, we explore five essential aspects of Azure Container Appsโeach highlighted in a one-minute video. From intelligent applications and secure networking to effortless deployments and rollbacks, these insights will help you maximize the capabilities of serverless containers on Azure. Azure Container Apps - in 1 Minute Azure Container Apps is a fully managed platform designed for cloud-native applications, providing effortless deployment and scaling. It eliminates infrastructure complexity, letting developers focus on writing code while Azure automatically handles scaling based on demand. Whether running APIs, event-driven applications, or microservices, Azure Container Apps ensures high performance and flexibility with minimal operational overhead. Watch the video on YouTube Intelligent Apps with Azure Container Apps โ in 1 Minute Azure Container Apps, Azure OpenAI, and Azure AI Search make it possible to build intelligent applications with Retrieval-Augmented Generation (RAG). Your app can call Azure OpenAI in real-time to generate and interpret data, while Azure AI Search retrieves relevant information, enhancing responses with up-to-date context. For advanced scenarios, AI models can execute live code via Azure Container Apps, and GPU-powered instances support fine-tuning and inferencing at scale. This seamless integration enables AI-driven applications to deliver dynamic, context-aware functionality with ease. Watch the video on YouTube Networking for Azure Container Apps: VNETs, Security Simplified โ in 1 Minute Azure Container Apps provides built-in networking features, including support for Virtual Networks (VNETs) to control service-to-service communication. Secure internal traffic while exposing public endpoints with custom domain names and free certificates. Fine-tuned ingress and egress controls ensure that only the right traffic gets through, maintaining a balance between security and accessibility. Service discovery is automatic, making inter-app communication seamless within your Azure Container Apps environment. Watch the video on YouTube Azure Continuous Deployment and Observability with Azure Container Apps - in 1 Minute Azure Container Apps simplifies continuous deployment with built-in integrations for GitHub Actions and Azure DevOps pipelines. Every code change triggers a revision, ensuring smooth rollouts with zero downtime. Observability is fully integrated via Azure Monitor, Log Streaming, and the Container Console, allowing you to track performance, debug live issues, and maintain real-time visibility into your appโs healthโall without interrupting operations. Watch the video on YouTube Effortless Rollbacks and Deployments with Azure Container Apps โ in 1 Minute With Azure Container Apps, every deployment creates a new revision, allowing multiple versions to run simultaneously. This enables safe, real-time testing of updates without disrupting production. Rolling back is instantโjust select a previous revision and restore your app effortlessly. This powerful revision control system ensures that deployments remain flexible, reliable, and low-risk. Watch the video on YouTube Watch the Full Playlist For a complete overview of Azure Container Apps capabilities, watch the full JavaScript on Azure Container Apps YouTube Playlist Create Your Own AI-Powered Video Content Inspired by these short-form technical videos? You can create your own AI-generated videos using Azure AI to automate scriptwriting and voiceovers. Whether youโre a content creator, or business looking to showcase technical concepts, Azure AI makes it easy to generate professional-looking explainer content. Learn how to create engaging short videos with Azure AI by following our open-source AI Video Playbook. Conclusion Azure Container Apps is designed to simplify modern application development by providing a fully managed, serverless container environment. Whether you need to scale microservices, integrate AI capabilities, enhance security with VNETs, or streamline CI/CD workflows, Azure Container Apps offers a comprehensive solution. By leveraging its built-in features such as automatic scaling, revision-based rollbacks, and deep observability, developers can deploy and manage applications with confidence. These one-minute videos provide a quick technical overview of how Azure Container Apps empowers you to build scalable, resilient applications with ease. FREE Content Check out our other FREE content to learn more about Azure services and Generative AI: Generative AI for Beginners - A JavaScript Adventure! Learn more about Azure AI Agent Service LlamaIndex on Azure JavaScript on Azure Container Apps JavaScript at MicrosoftConstruyendo una Aplicaciรณn Web con Inteligencia Artificial usando Python
En la segunda sesiรณn del GitHub Copilot Bootcamp LATAM, organizado por Microsoft Reactor, el ingeniero Manuel Ortiz, Embajador de Microsoft Learn y lรญder comunitario en GitHub, guiรณ a desarrolladores en la creaciรณn de una aplicaciรณn web con capacidades de inteligencia artificial. Este taller prรกctico combinรณ fundamentos de desarrollo backend en Python con tรฉcnicas avanzadas de integraciรณn de modelos de lenguaje de Azure OpenAI. Introducciรณn a Azure Open AI Azure Open AI es una colaboraciรณn entre Microsoft y OpenAI que permite a los desarrolladores integrar modelos avanzados de inteligencia artificial en sus aplicaciones utilizando la infraestructura de Azure. Esto ofrece acceso a modelos poderosos como GPT-4, que pueden ser utilizados para una variedad de tareas, desde procesamiento de lenguaje natural hasta generaciรณn de texto. Configuraciรณn de Azure Open AI Para comenzar a usar Azure Open AI, debes seguir algunos pasos bรกsicos: Crear una Cuenta en Azure: Si aรบn no tienes una cuenta, puedes crear una en el portal de Azure. Los estudiantes pueden solicitar crรฉditos gratuitos para usar los servicios de Azure. Crear un Servicio Azure Open AI: Accede al portal de Azure y busca "Azure Open AI". Haz clic en "Crear" y selecciona tu suscripciรณn y grupo de recursos. Elige la regiรณn y configura el nombre del servicio, que debe ser alfanumรฉrico y sin caracteres especiales. Selecciona el plan de precios adecuado y finaliza la creaciรณn del servicio. Obtener las Credenciales: Despuรฉs de crear el servicio, necesitarรกs las credenciales (clave de API y endpoint) para autenticar tus solicitudes. Estas credenciales se pueden encontrar en la secciรณn de "Claves y Endpoints" del servicio creado. Integraciรณn con Python y Flask Python es uno de los lenguajes de programaciรณn mรกs populares para el desarrollo de aplicaciones de inteligencia artificial debido a su simplicidad y vasta biblioteca de herramientas. Durante la configuraciรณn, puedes usar varias bibliotecas y herramientas que facilitan el desarrollo de IA con Python, incluyendo: TensorFlow: Una biblioteca de cรณdigo abierto para aprendizaje automรกtico. Keras: Una API de alto nivel para redes neuronales, que funciona sobre TensorFlow. Scikit-learn: Una biblioteca para aprendizaje automรกtico en Python. Flask: Un microframework para desarrollo de aplicaciones web. Una vez configurado el servicio Azure Open AI, puedes integrarlo en tus aplicaciones Python usando Flask. Aquรญ tienes un ejemplo de cรณmo hacerlo: Instalaciรณn de las Bibliotecas Necesarias: Crea un entorno virtual e instala las bibliotecas necesarias, como flask y openai. Configuraciรณn del Proyecto: Crea un archivo .env para almacenar tus credenciales de forma segura. Configura tu aplicaciรณn Flask para cargar estas credenciales y conectarse al servicio Azure Open AI. Creaciรณn del Modelo de IA: Utiliza la biblioteca openai para enviar prompts al modelo y recibir respuestas. Integra estas respuestas en tu aplicaciรณn web para proporcionar funcionalidades de IA a los usuarios. Ejemplo de Cรณdigo Aquรญ tienes un ejemplo simplificado de cรณmo configurar y usar Azure Open AI en una aplicaciรณn Flask: from flask import Flask, request, render_template import openai import os app = Flask(__name__) # Cargar las credenciales del archivo .env openai.api_key = os.getenv("AZURE_OPENAI_API_KEY") openai.api_base = os.getenv("AZURE_OPENAI_ENDPOINT") @app.route("/", methods=["GET", "POST"]) def index(): response_text = "" if request.method == "POST": prompt = request.form["prompt"] response = openai.Completion.create( engine="text-davinci-003", prompt=prompt, max_tokens=100 ) response_text = response.choices.text.strip() return render_template("index.html", response_text=response_text) if __name__ == "__main__": app.run(debug=True) Beneficios de Azure Open AI Acceso a Modelos Avanzados: Utiliza los modelos mรกs recientes y poderosos de OpenAI. Escalabilidad: La infraestructura de Azure permite escalar tus aplicaciones segรบn sea necesario. Seguridad y Conformidad: Benefรญciate de las robustas medidas de seguridad y conformidad de Azure. Sigue aprendiendo Si deseas aprender mรกs sobre estas tรฉcnicas, mira las grabaciones del GitHub Copilot Bootcamp, comienza a utilizar el GitHub Copilot gratuito y descubre cรณmo transformar tu manera de programar utilizando inteligencia artificial.GitHub Copilot 2025๋ 3์ ์ ๋ฐ์ดํธ
Visual Studio Code 2025๋ 2์ ๋ฆด๋ฆฌ์ค์ ์ค์ ๊ฒ์ ํ์ํฉ๋๋ค. ์ด๋ฒ ๋ฒ์ ์์๋ ๋ค์ํ ๊ธฐ๋ฅ์ ์ถ๊ฐํ์ผ๋ฉฐ, ํนํ GitHub Copilot๊ณผ ๊ด๋ จํ ์ฃผ์ ์ ๋ฐ์ดํธ๋ ๋ค์๊ณผ ๊ฐ์ต๋๋ค: Next Edit Suggestions (๋ฏธ๋ฆฌ๋ณด๊ธฐ) - Copilot์ด ๋ค์์ผ๋ก ์์ ํ ๊ฐ๋ฅ์ฑ์ด ๋์ ์ฝ๋๋ฅผ ์์ธกํฉ๋๋ค. Agent ๋ชจ๋ (๋ฏธ๋ฆฌ๋ณด๊ธฐ) - Copilot์ด ์๋์ผ๋ก ์์ ์ ์๋ฃํฉ๋๋ค. Copilot Edits์ ๋ ธํธ๋ถ ์ง์ - ๋ ธํธ๋ถ ํ์ผ์ ํธ์ง์ ์ฝ๊ฒ ํ ์ ์์ต๋๋ค. ์ฝ๋ ๊ฒ์ - Copilot์ด ์ฑํ ํ๋กฌํํธ์ ๊ด๋ จ๋ ํ์ผ์ ๊ฒ์ํฉ๋๋ค. ์ฌ์ฉ์ ์ง์ ์ง์นจ GA - Copilot์ ์ฌ์ฉ์์ ์๊ตฌ์ ๋ง๊ฒ ์ค์ ํ ์ ์์ต๋๋ค.
Learn how to develop innovative AI solutions with updated Azure skilling paths
The rapid evolution of generative AI is reshaping how organizations operate, innovate, and deliver value. Professionals who develop expertise in generative AI development, prompt engineering, and AI lifecycle management are increasingly valuable to organizations looking to harness these powerful capabilities while ensuring responsible and effective implementation. In this blog, weโre excited to share our newly refreshed series of Plans on Microsoft Learn that aim to supply your team with the tools and knowledge to leverage the latest AI technologies, including: Find the best model for your generative AI solution with Azure AI Foundry Create agentic AI solutions by using Azure AI Foundry Build secure and responsible AI solutions and manage generative AI lifecycles From sophisticated AI agents that can autonomously perform complex tasks to advanced chat models that enable natural human-AI collaboration, these technologies are becoming essential business tools rather than optional enhancements. Letโs take a look at the latest developments and unlock their full potential with our curated training resources from Microsoft Learn. Simplify the process of choosing an AI model with Azure AI Foundry Choosing the optimal generative AI model is essential for any solution, requiring careful evaluation of task complexity, data requirements, and computational constraints. Azure AI Foundry streamlines this decision-making process by offering diverse pre-trained models, fine-tuning capabilities, and comprehensive MLOps tools that enable businesses to test, optimize, and scale their AI applications while maintaining enterprise-grade security and compliance. Our Plan on Microsoft Learn titled Find the best model for your generative AI solution with Azure AI Foundry will guide you through the process of discovering and deploying the best models for creating generative AI solutions with Azure AI Foundry, including: Learn about the differences and strengths of various language models Find out how to integrate and use AI models in your applications to enhance functionality and user experience. Rapidly create intelligent, market-ready multimodal applications with Azure models, and explore industry-specific models. In addition, youโll have the chance to take part in a Microsoft Azure Virtual Training Day, with interactive sessions and expert guidance to help you skill up on Azure AI features and capabilities. By engaging with this Plan on Microsoft Learn, youโll also have the chance to prove your skills and earn a Microsoft Certification. Leap into the future of agentic AI solutions with Azure After choosing the right model for your generative AI purposes, our next Plan on Microsoft Learn goes a step further by introducing agentic AI solutions. A significant evolution in generative AI, agentic AI solutions enable autonomous decision-making, problem-solving, and task execution without constant human intervention. These AI agents can perceive their environment, adapt to new inputs, and take proactive actions, making them valuable across various industries. In the Create agentic AI solutions by using Azure AI Foundry Plan on Microsoft Learn, youโll find out how developing agentic AI solutions requires a platform that provides scalability, adaptability, and security. With pre-built AI models, MLOps tools, and deep integrations with Azure services, Azure AI Foundry simplifies the development of custom AI agents that can interact with data, make real-time decisions, and continuously learn from new information. Youโll also: Learn how to describe the core features and capabilities of Azure AI Foundry, provision and manage Azure AI resources, create and manage AI projects, and determine when to use Azure AI Foundry. Discover how to customize with RAG in Azure AI Foundry, Azure AI Foundry SDK, or Azure OpenAI Service to look for answers in documents. Learn how to use Azure AI Agent Service, a comprehensive suite of feature-rich, managed capabilities, to bring together the models, data, tools, and services your enterprise needs to automate business processes Thereโs also a Microsoft Virtual Training Day featuring interactive sessions and expert guidance, and you can validate your skills by earning a Microsoft Certification. Safeguard your AI systems for security and fairness Widespread AI adoption demands rigorous security, fairness, and transparency safeguards to prevent bias, privacy breaches, and vulnerabilities that lead to unethical outcomes or non-compliance. Organizations must implement responsible AI through robust data governance, explainability, bias mitigation, and user safety protocols, while protecting sensitive data and ensuring outputs align with ethical standards. Our third Plan on Microsoft Learn, Build secure and responsible AI solutions and manage generative AI lifecycles, is designed to introduce the basics of AI security and responsible AI to help increase the security posture of AI environments. Youโll not only learn how to evaluate and improve generative AI outputs for quality and safety, but youโll also: Gain an understanding of the basic concepts of AI security and responsible AI to help increase the security posture of AI environments. Learn how to assess and improve generative AI outputs for quality and safety. Discover how to help reduce risks by using Azure AI Content Safety to detect, moderate, and manage harmful content. Learn more by taking part in an interactive, expert-guided Microsoft Virtual Training Day to deepen your understanding of core AI concepts. Got a skilling question? Our new Ask Learn AI assistant is here to help Beyond our comprehensive Plans on Microsoft Learn, weโre also excited to introduce Ask Learn, our newest skilling innovation! Ask Learn is an AI assistant that can answer questions, clarify concepts, and define terms throughout your training experience. Ask Learn is your Copilot for getting skilled in AI, helping to answer your questions within the Microsoft Learn interface, so you donโt have to search elsewhere for the information. Simply click the Ask Learn icon at the top corner of the page to activate! Begin your generative AI skilling journey with curated Azure skilling Plans Azure AI Foundry provides the necessary platform to train, test, and deploy AI solutions at scale, and with the expert-curated skilling resources available in our newly refreshed Plans on Microsoft learn, your teams can accelerate the creation of intelligent, self-improving AI agents tailored to your business needs. Get started today! Find the best model for your generative AI solution with Azure AI Foundry Create agentic AI solutions by using Azure AI Foundry Build secure and responsible AI solutions and manage generative AI lifecycles
