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 variablesβall 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!Construyendo una AplicaciΓ³n Web con Inteligencia Artificial usando Python
En la segunda sesiΓ³n del GitHub Copilot Bootcamp LATAM, organizado por Microsoft Reactor, el ingeniero Manuel Ortiz, Embajador de Microsoft Learn y lΓder comunitario en GitHub, guiΓ³ a desarrolladores en la creaciΓ³n de una aplicaciΓ³n web con capacidades de inteligencia artificial. Este taller prΓ‘ctico combinΓ³ fundamentos de desarrollo backend en Python con tΓ©cnicas avanzadas de integraciΓ³n de modelos de lenguaje de Azure OpenAI. IntroducciΓ³n a Azure Open AI Azure Open AI es una colaboraciΓ³n 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 generaciΓ³n de texto. ConfiguraciΓ³n de Azure Open AI Para comenzar a usar Azure Open AI, debes seguir algunos pasos bΓ‘sicos: Crear una Cuenta en Azure: Si aΓΊn no tienes una cuenta, puedes crear una en el portal de Azure. Los estudiantes pueden solicitar crΓ©ditos 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 suscripciΓ³n y grupo de recursos. Elige la regiΓ³n y configura el nombre del servicio, que debe ser alfanumΓ©rico y sin caracteres especiales. Selecciona el plan de precios adecuado y finaliza la creaciΓ³n del servicio. Obtener las Credenciales: DespuΓ©s de crear el servicio, necesitarΓ‘s las credenciales (clave de API y endpoint) para autenticar tus solicitudes. Estas credenciales se pueden encontrar en la secciΓ³n de "Claves y Endpoints" del servicio creado. IntegraciΓ³n con Python y Flask Python es uno de los lenguajes de programaciΓ³n mΓ‘s populares para el desarrollo de aplicaciones de inteligencia artificial debido a su simplicidad y vasta biblioteca de herramientas. Durante la configuraciΓ³n, puedes usar varias bibliotecas y herramientas que facilitan el desarrollo de IA con Python, incluyendo: TensorFlow: Una biblioteca de cΓ³digo abierto para aprendizaje automΓ‘tico. Keras: Una API de alto nivel para redes neuronales, que funciona sobre TensorFlow. Scikit-learn: Una biblioteca para aprendizaje automΓ‘tico 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. AquΓ tienes un ejemplo de cΓ³mo hacerlo: InstalaciΓ³n de las Bibliotecas Necesarias: Crea un entorno virtual e instala las bibliotecas necesarias, como flask y openai. ConfiguraciΓ³n del Proyecto: Crea un archivo .env para almacenar tus credenciales de forma segura. Configura tu aplicaciΓ³n Flask para cargar estas credenciales y conectarse al servicio Azure Open AI. CreaciΓ³n del Modelo de IA: Utiliza la biblioteca openai para enviar prompts al modelo y recibir respuestas. Integra estas respuestas en tu aplicaciΓ³n web para proporcionar funcionalidades de IA a los usuarios. Ejemplo de CΓ³digo AquΓ tienes un ejemplo simplificado de cΓ³mo configurar y usar Azure Open AI en una aplicaciΓ³n 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 mΓ‘s recientes y poderosos de OpenAI. Escalabilidad: La infraestructura de Azure permite escalar tus aplicaciones segΓΊn sea necesario. Seguridad y Conformidad: BenefΓciate de las robustas medidas de seguridad y conformidad de Azure. Sigue aprendiendo Si deseas aprender mΓ‘s sobre estas tΓ©cnicas, mira las grabaciones del GitHub Copilot Bootcamp, comienza a utilizar el GitHub Copilot gratuito y descubre cΓ³mo transformar tu manera de programar utilizando inteligencia artificial.GitHub Copilot 2025λ 3μ μ λ°μ΄νΈ
Visual Studio Code 2025λ 2μ 릴리μ€μ μ€μ κ²μ νμν©λλ€. μ΄λ² λ²μ μμλ λ€μν κΈ°λ₯μ μΆκ°νμΌλ©°, νΉν GitHub Copilotκ³Ό κ΄λ ¨ν μ£Όμ μ λ°μ΄νΈλ λ€μκ³Ό κ°μ΅λλ€: Next Edit Suggestions (미리보기) - Copilotμ΄ λ€μμΌλ‘ μμ ν κ°λ₯μ±μ΄ λμ μ½λλ₯Ό μμΈ‘ν©λλ€. Agent λͺ¨λ (미리보기) - Copilotμ΄ μλμΌλ‘ μμ μ μλ£ν©λλ€. Copilot Editsμ λ ΈνΈλΆ μ§μ - λ ΈνΈλΆ νμΌμ νΈμ§μ μ½κ² ν μ μμ΅λλ€. μ½λ κ²μ - Copilotμ΄ μ±ν ν둬ννΈμ κ΄λ ¨λ νμΌμ κ²μν©λλ€. μ¬μ©μ μ§μ μ§μΉ¨ GA - Copilotμ μ¬μ©μμ μꡬμ λ§κ² μ€μ ν μ μμ΅λλ€.AI Sparks: AI Toolkit for VS Code - from playground to production
Are you building AI-powered applications from scratch or infusing intelligence into existing production code and systems? AI Sparks is your go-to webinar series for mastering the AI Toolkit (AITK) for VS Code from foundational concepts to cutting-edge techniques. In this bi-weekly, hands-on series, weβll cover: SLMs & Local Models β Test and deploy AI models and applications efficiently on your own terms locally, to edge devices or to the cloud Embedding Models & RAG β Supercharge retrieval for smarter applications using existing data. Multi-Modal AI β Work with images, text, and beyond. Agentic Frameworks β Build autonomous, decision-making AI systems. What will you learn from this session? Whether you're a developer, startup founder, or AI enthusiast, you'll gain practical insights, live demos, and actionable takeaways to level up your AI integration journey. Join us and spark your AI transformation! You can click here and register for the entire series on the reactor page Episode list You can also sign up for the individual episode and read about the topics covered using the following links: Feb 13 th 2025 β WATCH ON DEMAND @ Introduction to AI toolkit and feature walkthrough In In this episode, weβll introduce the AI Toolkit extension for VS Codeβa powerful way to explore and integrate the latest AI models from OpenAI, Meta, Deepseek, Mistral, and more. With this extension, you can browse state-of-the-art models, download some for local use, or experiment with others remotely. Whether you're enhancing an existing application or building something new, the AI Toolkit simplifies the process of selecting and integrating the right model for your needs. Feb 27 th 2025 β A short introduction to SLMs and local model with use cases In this episode, weβll explore Small Language Models (SLMs) and how they compare to larger models. SLMs are efficient, require less compute and memory, and can run on edge devices while still excelling at a variety of tasks. Weβll dive into the Phi-3.5 and Phi-4 model series and demonstrate how to build a practical application using these models. Mar 13 th 2025 β How to work with embedding models and build a RAG application In this episode, weβll dive into embedding modelsβimportant tools for working with vector databases and large language models. These models convert text into numerical representations, making it easier to process and retrieve information efficiently. After covering the core concepts, weβll apply them in practice by building a Retrieval-Augmented Generation (RAG) app using Small Language Models (SLMs) and a vector database. Mar 27 th 2025 β Multi-modal support and image analysis In this episode, weβll dig deeper into multi-modal capabilities of local and remote AI models and use visualization tools for better insights. Weβll also dive into multi-modal support in the AI Toolkit, showcasing how to process and analyze images alongside text. By the end, youβll see how these capabilities come together to enhance powerful AI applications. Apr 10 th 2025 β Evaluations β How to choose the best model for you applications needs In this episode, weβll explore how to evaluate AI models and choose the right one for your needs. Weβll cover key performance metrics, compare different models, and demonstrate testing strategies using features like Playground, Bulk Run, and automated evaluations. Whether you're experimenting with the latest models or transitioning to a new version, these evaluation techniques will help you make informed decisions with confidence. Apr 24 th 2025 β Agents and Agentic Frameworks In this episode, weβll explore agents and agentic frameworksβsystems that enable AI models to make decisions, take actions, and automate complex tasks. Weβll break down how these frameworks work, their practical applications, and how to build and integrate them into your projects. By the end, youβll have a clear understanding of how to build and leverage AI agents effectively. We will explore how to use and build agentic frameworks using AI Toolkit. Resources AI toolkit for VSCode - https://aka.ms/AIToolkit AI toolkit for VSCode Documentation - https://aka.ms/AIToolkit/doc Building Retrieval Augmented Generation (RAG) apps on VSCode & AI Toolkit Understanding and using Reasoning models such as DeepSeek R1 on AI toolkit - Using Ollama and OpenAI, Google and Anthropic hosted models with AI toolkit AI Sparks - YouTube PlaylistPrompt Engineering with GitHub Copilot and JavaScript
Mastering Prompt Engineering with GitHub Copilot & JavaScript AI-powered coding is here, and prompt engineering is the key to unlocking its full potential. π In this blog post, we explored how GitHub Copilot can enhance code generation, debugging, and API development while ensuring responsible AI use. From unit test automation to code optimization, we covered practical ways to boost productivity. Want to level up your skills? Join the GitHub Copilot Bootcamp and claim your discount on GitHub certification! #AI #GitHubCopilot #JavaScriptVS Codeμμ GitHub Copilotμ 무λ£λ‘ μ 곡ν©λλ€
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