The Launch of "AI Agents for Beginners": Your Gateway to Building Intelligent Systems
🌱 Getting Started Each lesson covers fundamental aspects of building AI Agents. Whether you're a novice or have some experience, you'll find valuable insights and practical knowledge. We also support multiple languages, so you can learn in your preferred language. To see the available languages, click here. If this is your first time working with Generative AI models, we highly recommend our "Generative AI For Beginners" course, which includes 21 lessons on building with GenAI. Remember to star (🌟) this repository and fork it to run the code! 📋 What You Need The course includes code examples that you can find in the code_samples folder. Feel free to fork this repository to create your own copy. The exercises utilize Azure AI Foundry and GitHub Model Catalogs for interacting with Language Models: Github Models - Free / Limited Azure AI Foundry - Azure Account Required We also leverage the following AI Agent frameworks and services from Microsoft: Azure AI Agent Service Semantic Kernel AutoGen For more information on running the code for this course, visit the Course Setup. 🙏 Want to Help? We welcome contributions from the community! If you have suggestions or spot any errors, please raise an issue or create a pull request. If you encounter any difficulties or have questions about building AI Agents, join our Azure AI Community on Discord. 📂 Each Lesson Includes A written lesson located in the README (Videos Coming March 2025) Python code samples supporting Azure AI Foundry and Github Models (Free) Links to extra resources to continue your learning 🗃️ Lessons Overview Intro to AI Agents and Use Cases Exploring Agentic Frameworks Understanding Agentic Design Patterns Tool Use Design Pattern Agentic RAG Building Trustworthy AI Agents Planning Design Pattern Multi-Agent Design Pattern Metacognition Design Pattern AI Agents in Production 🌐 Multi-Language Support We offer translations in several languages and will updating these on a regular basis. 🚀 Go Fork or Clone this repo and get started on your AI Agents journey 🤖 at https://aka.ms/ai-agents-beginners120 Days Study Plan to Become an AI-Focused Full-Stack Software Engineer
Hello there, my name is Oumaima, and I am an MLSA student ambassador from Morocco, studying at the University Of The People. Welcome to the first step in my exciting, unpredictable journey, one I’ve chosen to embark on with you! For the past three years, I’ve watched the AI industry evolve dramatically. Generative AI has shifted from a fascinating experiment to an integral part of our everyday lives, whether at school, work, or even in our personal routines. In fact, my ChatGPT app is now my go-to therapist, lawyer, and all-around advisor! As a software engineering student for over three years, I’ve seen the growth of generative AI up close. But this shift didn’t just inspire me; it made me realize that I don’t want to remain only a consumer of this technology. I want to contribute to it! Seeing AI’s ability to mimic human thought, draw connections from vast amounts of information, and deliver impressive results sparked something in me. It showed me that the best way to break into AI might just be to use AI itself as my guide. That’s when the idea came to ask ChatGPT O1-preview for a personalized study plan, crafted uniquely for me. It takes into account my available time, coding background, learning preferences, mental health, and energy. Here’s how my journey began with a simple prompt: I want to become an AI-focused full-stack software engineer and have 120 days to dedicate to this goal. Please create a detailed 120-day study plan tailored for me, dedicating 3-4 hours daily. The study plan should: - Cover all essential topics including programming foundations, data structures and algorithms (DS&A), mathematics for AI, machine learning fundamentals, deep learning, advanced AI topics, integrating AI into applications, web development basics for AI integration, advanced web development, full-stack project development, scripting, DevOps, and career development. - Include weekly breakdowns and daily tasks. - Provide recommended resources for each topic (e.g., online courses, tutorials, documentation). - Suggest hands-on projects or exercises to apply the concepts learned. - Incorporate tips for success, such as active engagement, seeking feedback, balancing depth and breadth, and maintaining well-being. - Emphasize developing all the skills that will make me an irreplaceable software developer, including scripting and DevOps skills. - Conclude with a summary and final advice. Please ensure the plan is structured, comprehensive, and practical for someone balancing work and study. Then it generated the following plan, that I tried to follow by using Microsoft Learn learning paths that offer in depth trainings on each topic I got: Days 1–25: Programming Foundations & Data Structures and Algorithms (DS&A) Microsoft Learn path suggestion: Python for beginners Days 26–50: Mathematics for AI & Machine Learning Fundamentals Microsoft Learn path suggestion: Introduction to machine learning Days 51–80: Deep Learning & Advanced AI Topics Microsoft Learn path suggestion: Train and evaluate deep learning models Days 81–100: Integrating AI into Applications Microsoft Learn path suggestion: Microsoft Azure AI Fundamentals: Generative AI Days 101–115: Advanced Web Development & Full-Stack Project Development Microsoft Learn path suggestion: Build an AI web app by using Python and Flask Days 116–120: Portfolio Projects and Industry Trends. Not going to lie, the roadmap turned out to be even more exciting than I’d expected! When I asked for it, I specified that it should guide me through developing problem-solving skills directly tied to full-stack development. I wanted a path that not only sharpens my abilities but also allows me to build interesting, hands-on applications where I can see the results of what I’m learning. And now, my friends, the journey has officially begun! I’ll be following the roadmap closely, documenting my weekly progress to learn AI, noting the challenges, and celebrating the accomplishments. The goal is to see if artificial intelligence can really help create a customized study plan that aligns with my personal goals, circumstances, and unique learning rhythm. So, stay tuned — this is only the beginning! See you in my first step with DSA!Prepare and get ready for AI-900 Certification
The Azure AI Fundamentals Training program is designed to provide a foundational understanding of artificial intelligence (AI) concepts and how AI services can be utilized on Azure. This training is ideal for individuals new to AI, aiming to build a solid understanding of AI concepts, practical applications, and the Responsible AI considerations involved. Throughout the program, participants will explore various AI services available on Azure. By the end of the program, attendees will be equipped with the knowledge to implement and manage AI solutions using Azure's tools and services The training program will run from 7th January, and we will have live sessions on YouTube and Discord from 8 - 9pm GMT+3 Earn a Certification Voucher Upon successful completion of the program, Kenyan participants will receive a certification voucher. By earning this certification, you'll be able to showcase your knowledge and skills to potential employers and colleagues, giving you a competitive edge in the job market. Key Program Takeaways Gain a foundational understanding of AI concepts and their applications. Explore Azure's AI services and tools. Learn how to implement practical AI solutions using Azure. Understand the responsible AI considerations and best practices for developing and deploying AI projects. Learner Checklist As a learner/participant, this is how you can participate in the program: Catch up and rewatch our previous AI sessions on Microsoft Reactor. Link: https://aka.ms/aifundamentalstraining-reactors Sign up and participate in the Microsoft Learn Challenge: https://aka.ms/aifundamentalstraining-csc - closing soon! Certification vouchers are up for grabs once you complete the challenge for all Kenyan participants. Once you sign up or complete the challenge, fill the form https://aka.ms/aifundamentalstraining-voucher to be eligible for a certification voucher. Join the Discord Community to interact with other learners: https://aka.ms/aifundamentalstraining-discord Sign up to the AINSI Skills Navigator to customize your learning journey at https://aka.ms/aifundamentalstraining-navigator Continue learning and exploring: https://aka.ms/aifundamentalstraining-collection Online Sessions Calendar The training program will run from 7th January, and we will have live sessions on YouTube and Discord from 8 - 9pm GMT+3 Week Topic Live Sessions Link Description 7 Jan Introduction to Artificial Intelligence and Azure AI Services YouTube Embark on a journey to explore the fundamentals of artificial intelligence (AI) with Azure. 9 Jan Microsoft Azure AI Fundamentals: Computer Vision YouTube Dive into the world of computer vision with Azure and discover how to harness the power of AI to analyze and interpret visual data. 14 Jan Microsoft Azure AI Fundamentals: Natural Language Processing YouTube Dive into the fascinating world of natural language processing (NLP) with Azure and learn how to build intelligent applications that can understand and interpret human language 16 Jan Generative AI pt 1 - Fundamentals of Generative AI YouTube Step into the world of generative AI with Azure and discover how to create new content such as text, images, music, and code using advanced AI models. 23 Jan Responsible generative AI YouTube Explore the principles and practices of responsible AI with Azure. 28 Jan Document Intelligence and Knowledge Mining YouTube Discover the power of Azure AI Search and learn how to build intelligent search solutions that can transform your data into actionable insights 29 Jan Generative AI pt 2 - Introduction to Azure AI Foundry YouTube Step into the world of generative AI with Azure and discover how to create new content such as text, images, music, and code using advanced AI models in Azure AI Foundry. 30 Jan Certification Readiness Session Discord Prepare to ace your Microsoft certification exams with this comprehensive walkthrough session. What are you waiting for? Rewatch and engage with live sessions https://aka.ms/aifundamentalstraining-reactors Join in and learn together with us! Remember, certification vouchers are up for grabs! Preparing for the AI-900 Certification can be a rewarding experience that opens up new opportunities in the field of AI and ML. By following the tips and utilizing the resources provided, you'll be well on your way to achieving your certification. Stay motivated, keep learning, and good luck on your journey to becoming AI-900 certified! All learning resources can be found at: MS Learn Collection: https://aka.ms/aifundamentalstraining-collection Enjoyed the session? Send us your feedback, the good, the bad and the ugly at: https://aka.ms/aifundamentalstraining-feedbackLearning Accelerators, Teams Assignments Generally Available for Blackboard by Anthology
Today we are pleased to announce general availability of the Teams Assignments integration for Blackboard by Anthology! Blackboard is the latest Learning Management System to move to GA with this powerful integration, with more on the way. The Teams Assignments integration brings the power of Microsoft Learning Accelerators and generative AI educator tools to your LMS, along with other engaging activities such as Flip videos, Auto-graded Forms, MakeCode projects, Whiteboards, and Reflect check-ins. Grades and feedback are automatically returned to the LMS gradebook. LMS admin resources for deployment: Blackboard Admin Deployment Guide NOTE: If you have already deployed the preview, you do not need to re-install - just keep enjoying the app! Educator resources for readiness: Using Microsoft Teams Assignments in Learning Management Systems Additional readiness for Microsoft Learning Accelerators: Support reading fluency practice with Reading Progress Develop confident presenters with Speaker Progress Develop search strategies with Search Coach and Search Progress Support building mathematics skills with Math Progress Build social and emotional skills in your classroom community with Reflect Achieve More with Assignments in your LMS The new Teams Assignments integration brings unique new capabilities into your LMS, saving educators time, and providing powerful tools to accelerate learning. For example, with Teams assignments you can: Leverage AI during assignment creation to rapidly draft assignment descriptions and rubrics Accelerate student learning and educator insights with Reading, Math, Search, and Speaker Progress assignments Deliver auto-graded quizzes with Microsoft Forms Access unique assignment types such as Microsoft Whiteboard, MakeCode, and Flip video Use Reflect exit check-ins to gain insights into student sentiment related to assignments Teams Assignments are seamlessly integrated with LMS assignments via the Learning Tools Interoperability® (LTI®) v1.3 Advantage standard. As an educator, you can create new Teams assignments or link existing Teams assignments in your LMS course. Once linked, you can view and access those assignments as you would any other assignment in the LMS course. Your students can view and access their Teams assignments in the same way as their other LMS assignments. Assignment grades in Teams are automatically imported into the LMS gradebook. You can now use the best that Teams assignments and your LMS offer working together, instead of working alongside each other. Support for additional LMS platforms will be added soon! You can sign up here to receive information on current and future LMS integration previews. Choose the Right Tool for the Right Assignment Many educators are already using the OneDrive LTI® tool to bring M365 documents into LMS assignments. The new Teams Assignments LTI® tool is a complementary solution that brings unique capabilities of Teams for Education to LMS users. When creating an assignment in your LMS, you should choose the tool that best meets the needs of the specific assignment. Whatever the choice of tool, the assignment can be viewed and accessed like any other assignment in the LMS, and grades will be added into the LMS gradebook. Teams Assignments LTI® OneDrive LTI® What is it best used for? Learning Accelerators, Forms Quizzes, Whiteboard, MakeCode, Flip video, and other activities exclusive to Teams Assignments. AI-assisted assignment and rubric creation. Multiple Word, PowerPoint, Excel documents along with other activities can be required to be submitted by the student as part of a single assignment. Embedding or linking M365 documents in course content, discussions, announcements, or other LMS content. When a single or several documents are the focus of the assignment, announcement or discussion, and other Teams Assignments features are not needed. Collaborative editing of documents. Where are assignments graded? In Microsoft Teams, with grades and feedback automatically syncing back to the LMS gradebook. In the LMS, using the native LMS rubrics and gradebook. Requires Microsoft Teams for Education? Yes No How to get Help or give Feedback For any issues deploying the integration, our Education Support team is here to help: Please contact https://aka.ms/EduSupport Once deployed, the Teams Assignments integration has links to Contact Support and Send Feedback from right within the app. These can be found in the user voice menu in the upper right on any view that appears within the LMS. Leverage Microsoft Education for more than Assignments in your LMS The new Teams assignments integration is the latest advancement in our continued efforts to bring the full value of Microsoft Education to users of learning management systems. You can already use the capabilities of Microsoft Teams, OneDrive, OneNote Class Notebooks, Microsoft Reflect directly within LMS courses. You can learn more about these integrations with leaning management systems such as Blackboard, Brightspace, Canvas, Schoology Learning and Moodle in our previous overview, and our support page. Learning Tools Interoperability® (LTI®) is a trademark of the 1EdTech Consortium, Inc. (www.1edtech.org)Data-driven Analytics for Responsible Business Solutions, a Power BI introduction course:
Want to gain inside on how students at Radboud University are introduced, in a praticle manner, to Power BI? Check out our learning process and final project. For a summary of our final solution watch our Video Blog and stick around till the end for some "wise words"!Using Advanced Reasoning Model on EdgeAI Part 1 - Quantization, Conversion, Performance
DeepSeek-R1 is very popular, and it can achieve the same capabilities as OpenAI o1 in advanced reasoning. Microsoft has also added DeepSeek-R1 models to Azure AI Foundry and GitHub Models. We can compare DeepSeek-R1 ith other available models through GitHub Models Playground Note This series revolves around deployment of SLMs to Edge Devices 'Edge AI' we will focus on the deployment advanced reasoning models, with different application scenarios. You can learn more in the following session AI Tour BRK453. In this experiement we want to deploy advanced reasoning models to the edge, so that they can run on edge devices with limited computing power and offline environments. At this time, the recommendation is to use the traditional ONNX model . We can use Microsoft Olive to convert the DeepSeek-R1 Distrill model. Getting started with Microsoft Olive is very straightforward. Install the Microsoft Olive library through the command line and Python 3.10+ (recommended) pip install olive-ai The DeepSeek-R1 Distrill model series has different parameters such as 1.5B, 7B, 8B, 14B, 32B, 70B, etc. This article is mainly based on the 1.5B, 7B, and 14B models (so a Small Language Model). CPU Inference Let's discuss 1.5B and 7B, which are models with lower parameter. We can directly use the CPU as computing for inference to test the effect (hardware environment Azure DevBox, AMD EPYC 7763 64-Core + 64GB Memory + 2T SSD) Quantization conversion olive auto-opt --model_name_or_path <Your DeepSeek-R1-Distill-Qwen-1.5B/7B local location> --output_path <Your Convert ONNX INT4 Model local location> --device cpu --provider CPUExecutionProvider --precision int4 --use_model_builder --log_level 1 You can download it directly from my Hugging face Repo (Note: This model is for testing and has not been fully tested by AI Content Safety or provided as an Offical Model) DeepSeek-R1-Distill-Qwen-1.5B-ONNX-INT4-CPU DeepSeek-R1-Distill-Qwen-7B-ONNX-INT4-CPU Running with ONNX Runtime GenAI Install ONNX Runtime GenAI and ONNX Runtime CPU support libraries pip install onnxruntime-genai pip install onnxruntime Sample Code https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/demo-1.5b.ipynb https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/demo-7b.ipynb Performance comparison 1.5B vs 7B We compare two different inference scenarios explain 1+1=2 1.5B quantized ONNX model memory occupied, time consumption and number of tokens generated: 7B quantized ONNX model memory occupied, time consumption and number of tokens generated 2. Find all pairwise different isomorphism groups with order 147 and no elements with order 49 1.5B quantized ONNX model memory occupied, time consumption and number of tokens generated: 7B quantized ONNX model memory occupied, time consumption and number of tokens generated Results of the numbers Through the test, we can see that the 1.5B model of DeepSeek is more suitable for use on CPU inference and can be deployed on traditional PCs or IoT devices. As for 7B, although it has better inference, it is not very effective on CPU operation. GPU Inference It is ideal if we have a GPU on the edge device. We can quantize and convert it to an ONNX model for CPU inference through Microsoft Olive. Of course, it can also be converted to a model for GPU inference. Here I take the 14B DeepSeek-R1-Distill-Qwen-14B as an example and make an inference comparison with Microsoft's Phi-4-14B Quantization conversion olive auto-opt --model_name_or_path <Your Phi-4-14B or DeepSeek-R1-Distill-Qwen-14B local path > --output_path <Your converted Phi-4-14B or DeepSeek-R1-Distill-Qwen-14B local path > --device gpu --provider CUDAExecutionProvider --precision int4 --use_model_builder --log_level 1 You can download it directly from my Hugging face Repo (Note: This model is for testing and has not been fully tested by AI Content Safety and not an Official Model) DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU Phi-4-14B-ONNX-INT4-GPU Running with ONNX Runtime GenAI CUDA Install ONNX Runtime GenAI and ONNX Runtime GPU support libraries pip install onnxruntime-genai-cuda pip install onnxruntime-gpu Compare the results in the GPU environment with Gradio It is recommended to use a GPU with more than 8G memory To increase the comparison of the results, we compare it with Phi-4-14B-ONNX-INT4-GPU and DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU to see the different results. We also show we use OpenAI o1-mini (it is recommended to use o1-mini through GitHub Models), Sample Code https://github.com/kinfey/EdgeAIForAdvancedReasoning/blob/main/notebook/Performance_AdvancedReasoning_ONNX_CPU.ipynb You can test any prompt on Gradio to compare the results of Phi-4-14B-ONNX-INT4-GPU, DeepSeek-R1-Distill-Qwen-14B-ONNX-INT4-GPU and OpenAI o1 mini. DeepSeek-R1 reduces the cost of inference models and produces more instructive results on professional problems, but Phi-4-14B also has advantages in reasoning and uses lower computing power to complete inference. As for OpenAI o1 mini, it is more comprehensive and can touch all problems. If you want to deploy to Edge Device, Phi-4-14B and quantized DeepSeek-R1 are good choices for you. This blog is just a simple test and the first in this series. Please share your feedback and continue the discussion in the Microsoft AI Discord Channel. Feel free to me a message or comment. We look forward to sharing more around the opportunity of EdgeAI and more content in this series. Resource DeepSeek-R1 in GitHub Models https://github.com/marketplace/models/azureml-deepseek/DeepSeek-R1 DeepSeek-R1 in Azure AI Foundry https://ai.azure.com/explore/models/DeepSeek-R1/version/1/registry/azureml-deepseek Phi-4-14B in Hugging face https://huggingface.co/microsoft/phi-4 Learn about Microsoft Olive https://github.com/microsoft/olive Learn about ONNX Runtime GenAI https://github.com/microsoft/onnxruntime-genai Microsoft AI Discord Channel BRK453 Exploring cutting-edge models: LLMs, SLMs, local development and more https://aka.ms/aitour/brk453Utilizando Slash commands en GitHub Copilot para Visual Studio
En este blog, demostraremos más información de los comandos de barra diagonal (slash commands). Como los llama Bruno Capuano en un video, "pequeños hechizos mágicos"; en otras palabras, al escribir una barra diagonal (/) en un símbolo del sistema de GitHub Copilot, se abre una opción en la que puede elegir algunos comandos que tendrán una acción predefinida. [Blog original en inglés creado por Laurent Bugnion y Bruno Capuano] Abriendo el menú de los comandos “Slash” Para abrir el menú de comandos de barra diagonal, puedes hacer clic en el botón Barra diagonal dentro de la ventana de chat de GitHub Copilot, como se muestra en la imagen inferior. Otra opción es simplemente escribir una barra diagonal en el área de GitHub Copilot Chat. Cualquiera de las dos acciones abrirá el menú que se ve así: Repasemos los comandos: doc: Este comando ayuda a crear un comentario de documentación relacionado con la selección determinada. Por ejemplo, si el cursor está dentro de un método, GitHub Copilot propondrá un comentario para este método. exp: Este comando comienza un nuevo hilo de conversación, con un contexto completamente nuevo. Después, puedes cambiar entre conversaciones desde un cuadro combinado en la parte superior de la ventana de chat. explain: Este comando explicará una parte del código. Si seleccionas un código, GitHub Copilot te explicará este código. También puedes utilizar el comando # para especificar un contexto diferente. fix: Este comando propondrá una corrección para el código seleccionado. generate: Este comando generará un código correspondiente a la pregunta que acabas de hacer. help: Este comando mostrará ayuda sobre GitHub Copilot. optimize: Este comando analizará el código en contexto y propondrá una optimización (en términos de rendimiento, líneas de código, etc.). tests: Este comando creará una prueba unitaria para el código seleccionado. Obtendremos más detalles sobre cada uno de estos comandos en futuras publicaciones. Más información Como siempre, puedes encontrar más información, en nuestra colección de Microsoft Learn. Mantente al tanto de este blog para obtener más contenido. Y, por supuesto, ¡también puedes suscribirte a nuestro canal de YouTube!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.How to Customize Visual Studio Code Chat with GitHub Copilot and Semantic Kernel
Discover how to customize Visual Studio Code Chat to revolutionize your development workflow with AI. By leveraging GitHub Copilot, Semantic Kernel, and Azure AI Agent Service, you can create chat participants tailored to tasks like project creation, requirement analysis, and code orchestration. Learn to integrate models like o1-mini for reasoning and .NET Aspire for distributed application management. Empower your IDE with AI to streamline complex workflows and boost efficiency.
