Exploring Azure OpenAI Assistants and Azure AI Agent Services: Benefits and Opportunities
In the rapidly evolving landscape of artificial intelligence, businesses are increasingly turning to cloud-based solutions to harness the power of AI. Microsoft Azure offers two prominent services in this domain: Azure OpenAI Assistants and Azure AI Agent Services. While both services aim to enhance user experiences and streamline operations, they cater to different needs and use cases. This blog post will delve into the details of each service, their benefits, and the opportunities they present for businesses. Understanding Azure OpenAI Assistants What Are Azure OpenAI Assistants? Azure OpenAI Assistants are designed to leverage the capabilities of OpenAI's models, such as GPT-3 and its successors. These assistants are tailored for applications that require advanced natural language processing (NLP) and understanding, making them ideal for conversational agents, chatbots, and other interactive applications. Key Features Pre-trained Models: Azure OpenAI Assistants utilize pre-trained models from OpenAI, which means they come with a wealth of knowledge and language understanding out of the box. This reduces the time and effort required for training models from scratch. Customizability: While the models are pre-trained, developers can fine-tune them to meet specific business needs. This allows for the creation of personalized experiences that resonate with users. Integration with Azure Ecosystem: Azure OpenAI Assistants seamlessly integrate with other Azure services, such as Azure Functions, Azure Logic Apps, and Azure Cognitive Services. This enables businesses to build comprehensive solutions that leverage multiple Azure capabilities. Benefits of Azure OpenAI Assistants Enhanced User Experience: By utilizing advanced NLP capabilities, Azure OpenAI Assistants can provide more natural and engaging interactions. This leads to improved customer satisfaction and loyalty. Rapid Deployment: The availability of pre-trained models allows businesses to deploy AI solutions quickly. This is particularly beneficial for organizations looking to implement AI without extensive development time. Scalability: Azure's cloud infrastructure ensures that applications built with OpenAI Assistants can scale to meet growing user demands without compromising performance. Understanding Azure AI Agent Services What Are Azure AI Agent Services? Azure AI Agent Services provide a more flexible framework for building AI-driven applications. Unlike Azure OpenAI Assistants, which are limited to OpenAI models, Azure AI Agent Services allow developers to utilize a variety of AI models, including those from other providers or custom-built models. Key Features Model Agnosticism: Developers can choose from a wide range of AI models, enabling them to select the best fit for their specific use case. This flexibility encourages innovation and experimentation. Custom Agent Development: Azure AI Agent Services support the creation of custom agents that can perform a variety of tasks, from simple queries to complex decision-making processes. Integration with Other AI Services: Like OpenAI Assistants, Azure AI Agent Services can integrate with other Azure services, allowing for the creation of sophisticated AI solutions that leverage multiple technologies. Benefits of Azure AI Agent Services Diverse Use Cases: The ability to use any AI model opens a world of possibilities for businesses. Whether it's a specialized model for sentiment analysis or a custom-built model for a niche application, organizations can tailor their solutions to meet specific needs. Enhanced Automation: AI agents can automate repetitive tasks, freeing up human resources for more strategic activities. This leads to increased efficiency and productivity. Cost-Effectiveness: By allowing the use of various models, businesses can choose cost-effective solutions that align with their budget and performance requirements. Opportunities for Businesses Improved Customer Engagement Both Azure OpenAI Assistants and Azure AI Agent Services can significantly enhance customer engagement. By providing personalized and context-aware interactions, businesses can create a more satisfying user experience. For example, a retail company can use an AI assistant to provide tailored product recommendations based on customer preferences and past purchases. Data-Driven Decision Making AI agents can analyze vast amounts of data and provide actionable insights. This capability enables organizations to make informed decisions based on real-time data analysis. For instance, a financial institution can deploy an AI agent to monitor market trends and provide investment recommendations to clients. Streamlined Operations By automating routine tasks, businesses can streamline their operations and reduce operational costs. For example, a customer support team can use AI agents to handle common inquiries, allowing human agents to focus on more complex issues. Innovation and Experimentation The flexibility of Azure AI Agent Services encourages innovation. Developers can experiment with different models and approaches to find the most effective solutions for their specific challenges. This culture of experimentation can lead to breakthroughs in product development and service delivery. Enhanced Analytics and Insights Integrating AI agents with analytics tools can provide businesses with deeper insights into customer behavior and preferences. This data can inform marketing strategies, product development, and customer service improvements. For example, a company can analyze interactions with an AI assistant to identify common customer pain points, allowing them to address these issues proactively. Conclusion In summary, both Azure OpenAI Assistants and Azure AI Agent Services offer unique advantages that can significantly benefit businesses looking to leverage AI technology. Azure OpenAI Assistants provide a robust framework for building conversational agents using advanced OpenAI models, making them ideal for applications that require sophisticated natural language understanding and generation. Their ease of integration, rapid deployment, and enhanced user experience make them a compelling choice for businesses focused on customer engagement. Azure AI Agent Services, on the other hand, offer unparalleled flexibility by allowing developers to utilize a variety of AI models. This model-agnostic approach encourages innovation and experimentation, enabling businesses to tailor solutions to their specific needs. The ability to automate tasks and streamline operations can lead to significant cost savings and increased efficiency. Additional Resources To further explore Azure OpenAI Assistants and Azure AI Agent Services, consider the following resources: Agent Service on Microsoft Learn Docs Watch On-Demand Sessions Streamlining Customer Service with AI-Powered Agents: Building Intelligent Multi-Agent Systems with Azure AI Microsoft learn Develop AI agents on Azure - Training | Microsoft Learn Community and Announcements Tech Community Announcement: Introducing Azure AI Agent Service Bonus Blog Post: Announcing the Public Preview of Azure AI Agent Service AI Agents for Beginners 10 Lesson Course https://aka.ms/ai-agents-beginners
How to Optimize your Codespaces: Pro-tips for managing quotas
Now that GitHub Codespaces is free for anyone, you might be surprised to see how fast you can hit the free quota. Here are four things you can do to make the most out of the 90 hours you get every month (and 180 hours if you are a student).Redeeming Azure for Student from your GitHub Student Pack when you do not have an Academic Email
GitHub Student Developer Pack Learn to ship software like a pro. There's no substitute for hands-on experience. But for most students, real world tools can be cost-prohibitive. That's why we created the GitHub Student Developer Pack with some of our partners and friends. Sign up for Student Developer PackMicrosoft's Student Opportunities: A Gateway to Professional Growth
Are you a student looking to give your career in tech a boost? Look no further than Microsoft's student opportunities. From scholarships to internships, Microsoft provides a range of programs designed to help students develop their skills, gain practical experience, and build connections in the industry. In this article, we'll explore Microsoft's opportunities and events, and how they can be the gateway to professional growth for students seeking a career in technology.Why Every JavaScript Developer Should Try TypeScript
Introduction "Why did the JavaScript developer break up with TypeScript?" "Because they couldn’t handle the commitment!" As a student entrepreneur, you're constantly juggling coursework, projects, and maybe even a startup idea. You don’t have time to debug mysterious JavaScript errors at 2 AM. That's where TypeScript comes in helping you write cleaner, more reliable code so you can focus on building, not debugging. In this post, I’ll show you why TypeScript is a must-have skill for any student developer and how it can set your projects up for success. Overview of TypeScript JavaScript, the world's most-used programming language, powers cross-platform applications but wasn't designed for large-scale projects. It lacks some features needed for managing extensive codebases, making it challenging for IDEs. TypeScript overcomes these limitations while preserving JavaScript’s versatility, ensuring code runs seamlessly across platforms, browsers, and hosts. What is TypeScript? TypeScript is an open-source, strongly typed superset of JavaScript that compiles down to regular JavaScript. Created by Microsoft, it introduces static typing, interfaces, and modern JavaScript features, making it a favorite for both small projects and enterprise applications Why Should Student Entrepreneurs Care About TypeScript? TypeScript Saves You Time: You know that feeling when your JavaScript app breaks for no reason just before a hackathon deadline? TypeScript catches errors before your code even runs, so you don’t waste hours debugging. TypeScript Makes Your Code More Professional: If you're building a startup, investors and potential employers will look at your code. TypeScript makes your projects scalable, readable, and industry ready. TypeScript Helps You Learn Faster: As a student, you’re still learning. Typescripts autocomplete and type hints guide you, reducing the number of Google searches you need. For a beginner-friendly introduction to TypeScript, check out this MS Learn module: 🔗 Introduction to TypeScript Setting Up TypeScript in 5 Minutes Prerequisites Knowledge of JavaScript NodeJS Code editor Visual Studio Code Install TypeScript TypeScript is available as a package in the npm registry as typescript. To install the latest version of TypeScript: In the Command Prompt window, enter npm install -g typescript. npm install -g typescript Enter tsc to confirm that TypeScript is installed. If it was successfully installed, this command should show a list of compiler commands and options. Create a new TypeScript file Create a new folder in your desktop called “demo”, right-click on the folder icon and select open with vs code When vs code opens, click on add file icon and create new file “index.ts” Let’s write a simple function to add two numbers Compile a TypeScript file TypeScript is a strict superset of ECMAScript 2015 (ECMAScript 6 or ES6). All JavaScript code is also TypeScript code, and a TypeScript program can seamlessly consume JavaScript. You can convert a JavaScript file to a TypeScript file just by renaming the extension from .js to .ts. However, not all TypeScript code is JavaScript code. TypeScript adds new syntax to JavaScript, which makes the JavaScript easier to read and implements some features, such as static typing. You transform TypeScript code into JavaScript code by using the TypeScript compiler. You run the TypeScript compiler at the command prompt by using the tsc command. When you run tsc with no parameters, it compiles all the .ts files in the current folder and generates a .js file for each one. To compile our code, open command prompt in vs code and type tsc index.ts Notice that a new JavaScript file has been added, You might need to refresh the Explorer pane to view the file At the Terminal command prompt, enter node index.js. This command runs the JavaScript and displays the result in the console log. And that’s it! 🎉 Core TypeScript Features Every Developer Should Know Static Typing for Safer Code – TypeScript’s static typing prevents runtime errors by catching type mismatches at compile time, making code more reliable. This prevents unintended assignments like: Interfaces for Better Object Structures – Interfaces help define the structure of objects, ensuring consistency and maintainability across a codebase. Enums for Readable Constants – Enums define named constants, making code more readable and reducing the risk of using incorrect values. Generics for Reusable Code – Generics allow you to create flexible, type-safe functions and components that work with multiple data types. Type Assertions for Flexibility – Type assertions let you explicitly specify a value’s type when TypeScript cannot infer it correctly, enhancing type safety in dynamic scenarios. Conclusion: TypeScript is Your Superpower🚀 TypeScript is more than just a superset of JavaScript—it's a game-changer for developers, especially those working on large-scale projects or building career-defining applications. By introducing static typing, interfaces, Enums, generics, and type assertions, TypeScript helps eliminate common JavaScript pitfalls while maintaining flexibility. These features not only enhance code quality and maintainability but also improve collaboration among teams, ensuring that projects scale smoothly. Whether you're a student entrepreneur, a freelancer, or a professional developer, adopting TypeScript early will give you a competitive edge in the industry. Embracing TypeScript means writing safer, cleaner, and more efficient code without sacrificing JavaScript’s versatility. With its powerful developer tools and seamless integration with modern frameworks, TypeScript ensures that your code remains robust and adaptable to changing requirements. As the demand for TypeScript continues to grow, learning and using it in your projects will open new opportunities and set you apart in the ever-evolving world of web development. Read More And do more with Typescript Declare variables in Typescript TypeScript repository on GitHub TypeScript tutorial in Visual Studio Code Build JavaScript applications using TypeScriptUtilizando 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 use GitHub Copilot for Azure?
Good news for everyone - GitHub Copilot is now available for free in VS Code!! Excited to try GitHub copilot for Azure in VSCode? Prerequisites: Account in GitHub Sign up for GitHub Copilot Account in Azure Install VSCode Step 1. Installation How to install GitHub Copilot for Azure? Open VS Code, in the leftmost panel, click on Extensions, type – ‘GitHub copilot for azure’, and install the first result which is by Microsoft. As shown in the Fig. 1.1 below: Fig. 1.1 How to install GitHub Copilot for Azure in VSCode 2. After this installation, you will be prompted to install – GitHub Copilot, Azure Tools, and other required installations. Click on allow and install all required extensions from the same method, as used above. Fig. 1.1.1 Installation of GitHub Copilot and sign in with GitHub Step 2: Enable How to enable GitHub Copilot in GitHub? Open GitHub, Click on top rightmost Profile pic, a left panel will open. Click on Your Copilot. Fig. 1.2 Locate GitHub Copilot Upon opening, enable it for IDE, as shown in the below Fig. 1.3 Fig. 1.3 Enabling Copilot Chat in the IDE Step 3: Walkthrough Open VSCode, and click on the GitHub Copilot icon from topmost right side. This will open the GitHub Copilot Chat. From here, you can customize the model type and Send commands. Type azure to work with Azure related tasks. Below Fig. 1.4 will help to locate the things smoothly: Fig. 1.4 Locating GitHub Copilot Chat in VSCode Scenario: Using the GitHub Repository If you have any of your project already available in the GitHub public repository, then paste the link of it in the chat section and append it with the below prompt: Prompt: This is my website deployed locally in GitHub, help me deploy in Azure. Hit Enter from the keyboard or Click the arrow sign, and proceed further with the instructions generated by the Copilot. Note: You will be prompted to Authenticate your Azure Account, simply follow the instructions said to authenticate. If you don’t have any website, paste the prompt written below in the chat section: Prompt: Could you help me create and deploy a simple Flask website by using an azd template? Fig. 1.5 Reply from GitHub Copilot for Azure As visible in the above Fig. 1.5, the GitHub Copilot for Azure will send template in the response. Hover the arrow over it, and then click on Insert into terminal, this will automatically insert the command in the terminal. Meanwhile, you may need to Authenticate your Azure Account, simply follow the instructions said to authenticate. It will take a few minutes to initialize. Meanwhile, answer the questions it asks, if unsure, simply ask the same question by copy pasting in the GitHub Copilot Chat, and it will guide you. You can ask more questions like: What does azd init command do? How the website will be deployed? What region, should I select? Once, you are clear with all of the doubts, type azd up command in the terminal, this will deploy the website in azure. Fig. 1.6 GitHub Copilot guiding the user to deploy This Command will ask which subscription you want to use to deploy your website. Fig. 1.7 Finding Subscription in Azure Portal Open the Azure portal, and type subscription in the search bar, as visible in Fig. 1.7. Click the first result and copy paste the Subscription ID visible there, to the GitHub Copilot chat, and append something like below: <Subscription ID> This is my Azure Subscription ID, deploy my website using it. <I reside in <Country Name> Once, done, you would be able to view the deployed website along with the new resources created in the Azure Portal. To un-deploy it, to free up the Azure resources, ask the same to GitHub Copilot, and it will guide you further! Tips and Tricks: For any error or Questions, directly ask to GitHub Copilot for Azure and it will answer your all queries, no limit! If unsure about anything, just paste the subscription id and share your country in the chat to get customized queries to run! Summary: GitHub Copilot can be used in VS Code for free, by installing thru extensions tab of VS Code. The deployment is done using just 2 commands: azd init and azd up To un-deploy, simply visit the directory and type azd down Happy 2025 with unlimited experiments using GitHub Copilot for Azure @VSCode for free!Why Every Engineering Student Should Be on GitHub!
Why Every Engineering Student Should Be on GitHub! GitHub is an essential tool for engineering students stepping into the coding world. It serves as a digital playground where students can manage code, collaborate globally, and contribute to open-source projects. With GitHub, students can track their coding progress, work on group projects, and showcase their portfolios to potential employers. A standout feature is the GitHub Student Developer Pack, which provides free resources like software, cloud services, and developer tools. By signing up and verifying student status, users can access these valuable tools and leverage GitHub to enhance their learning and coding skills. Stay tuned for insights on GitHub Copilot, the AI-powered coding companion.Make your own private ChatGPT
Introduction Creating your own private ChatGPT allows you to leverage AI capabilities while ensuring data privacy and security. This guide walks you through building a secure, customized chatbot using tools like Azure OpenAI, Cosmos DB and Azure App service. Why Build a Private ChatGPT? With the rise of AI-driven applications, organizations, people often face challenges related to data privacy, customization, and integration. Building a private ChatGPT addresses these concerns by: Maintaining Data Privacy: Keep sensitive information within your infrastructure. Customizing Responses: Tailor the chatbot’s behavior and language to suit your requirements. Ensuring Security: Leverage enterprise-grade security protocols. Avoiding Data Sharing: Prevent your data from being used to train external models. If organizations do not take these measures their data may go into future model training and can leak your sensitive data to public. Eg: Chatgpt collects personal data mentioned in their privacy policy Prerequisites Before you begin, ensure you have: Access to Azure OpenAI Service. A development environment set up with Python. Basic knowledge of FastAPI and MongoDB. An Azure account with necessary permissions. If you do not have Azure subscription, try Azure for students for FREE. Step 1: Set Up Azure OpenAI Log in to the Azure Portal and create an Azure OpenAI resource. Deploy a model, such as GPT-4o (multimodal), and note down the endpoint and API key. Note there is also an option of keyless authentication. Configure permissions to control access. Step 2: Use Chatgpt like app sample You can select any repository to be as base template for your app, in this I will be using the third option AOAIchat. It is developed by me. GitHub - mckaywrigley/chatbot-ui: AI chat for any model. Azure-Samples/azure-search-openai-demo: A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. sourabhkv/AOAIchat: Azure OpenAI chat This architecture diagram represents a typical flow for a private ChatGPT application with the following components: App UX (User Interface): This is the front-end application (mobile, web, or desktop) where users interact with the chatbot. It sends the user's input (prompt) and displays the AI's responses. App Service: Acts as the backend application, handling user requests and coordinating with other services. Functions: Receives user inputs and prepares them for processing by the Azure OpenAI service. Streams AI responses back to the App UX. Reads from and writes to Cosmos DB to manage chat history. Azure OpenAI Service: This is the core AI service, processing the user input and generating responses using models like GPT-4o. The App Service sends the user input (along with context) to this service and receives the AI-generated responses. Cosmos DB: A NoSQL database used to store and manage chat history. Operations: Writes user messages and AI-generated responses for future reference or analysis. Reads chat history to provide context for AI responses, enabling more intelligent and contextual conversations. Data Flow: User inputs are sent from the App UX to the App Service. The App Service forwards the input (with additional context, if needed) to Azure OpenAI. Azure OpenAI generates a response, which is streamed back to the App UX via the App Service. The App Service writes user inputs and AI responses to Cosmos DB for persistence. This architecture ensures scalability, secure data handling, and the ability to provide contextual responses by integrating database and AI services. What can you do with my template? AOAIchat supports personal, enterprise chat enabled by RAG People can enable RAG mode if they want to search within their database, else it behaves like normal ChatGPT. It supports multimodality, (supports image, text input) also depends on model deployed in Azure AI foundry. Step 3: Deploy to Azure Deploy a Cosmos DB account in nearest region Deploy Azure OpenAI model (gpt-4o, gpt-4o-mini recommended) Deploy Azure App service, try using container I would recommend B1plan to your nearest region, select docker registry sourabhkv/aoaichatdb:0.1 startup command uvicorn app:app --host 0.0.0.0 --port 80 After app service starts, put all environment variables The application requires the following environment variables to be set for proper configuration: Environment Variable Description AZURE_OPENAI_ENDPOINT The endpoint for Azure OpenAI API. AZURE_OPENAI_API_KEY API key for accessing Azure OpenAI. DEPLOYMENT_NAME Azure OpenAI deployment name. API_VERSION API version for Azure OpenAI. MAX_TOKENS Maximum tokens for API responses. MONGO_DETAILS MongoDB connection string. AZURE_OPENAI_ENDPOINT=<your_azure_openai_endpoint> AZURE_OPENAI_API_KEY=<your_azure_openai_api_key> DEPLOYMENT_NAME=<your_deployment_name> API_VERSION=<your_api_version> MAX_TOKENS=<max_tokens> MONGO_DETAILS=<your_mongo_connection_string> Optional feature: implement authentication to secure access. Within app service select Authentication and select service providers. I went with Entra based authentication with single tenant. There is option of multi-tenant, personal accounts as well. Restart App service and within 2 minutes your private ChatGPT is ready. Pricing Pricing may depend on the plan you have deployed resources and region. Check Azure calculator for price estimation. My estimate for pricing I deployed all my resources in Sweden central Cosmos DB config - Cosmos DB for MongoDB (RU) serverless config with single write master, 2 GB transactional storage, 2 backup plan (FREE) ~ 0.75$ Azure OpenAI service - plan S0, model gpt-4o-mini global deployment, Input 20000 tokens, Output 10000 tokens ~ 9.00$ App service plan - OS Linux, Tier B1, instance count 1 ~13.14$ Total monthly cost = 22.89$ This price may vary in future, in region I calculated my configuration in Azure calculator Governance Azure OpenAI provides content filters to block any kind of input that violates responsible AI practices. Categories include Hate and Fairness Sexual Violence Self-harm User Prompt Attacks (direct and indirect) The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Azure OpenAI Service includes default safety settings applied to all models set as medium. Content filters can be modified to different level depending on use case. It supports RAG, I have provided detailed solution for it in my GitHub. Practical implementation GE Aerospace, in partnership with Microsoft and Accenture, has launched a company-wide generative AI platform, leveraging Microsoft Azure and Azure OpenAI Service. This solution aims to transform asset tracking and compliance in aviation, enabling quick access to maintenance records and reducing manual processing time from days to minutes. It supports informed decision-making by providing insights into aircraft leasing, compliance gaps, and asset health. For enterprises implementing private ChatGPT solutions, this illustrates the potential of generative AI for streamlining document-intensive processes while ensuring data security and compliance through cloud-based infrastructure like Azure. GE Aerospace Launches Company-wide Generative AI Platform for Employees | GE Aerospace News Build your own private ChatGPT style app with enterprise-ready architecture - By Microsoft Mechanics How to make private ChatGPT for FREE? It can be FREE if all of the setup is running locally on your hardware. Cosmos DB <-> MongoDB. Azure OpenAI <-> Ollama / LM studio Refer this NOTE : I have used gpt-4o, gpt-4o-mini these values are hardcoded in webpage, if you are using other models, you might have to change them in index.html. App Service <-> Local machine Register for Github models to access API for FREE. Note: GitHub models have rate limit for different models. Useful links sourabhkv/AOAIchat: Azure OpenAI chat What is RAG? Get started with Azure OpenAI API Chat with Azure OpenAI models using your own dataTiny But Mighty: Unleashing the Power of Small Language Models 🚀
While Large Language Models (LLMs) like GPT-4 dominate headlines with their extensive capabilities, they often come at the cost of high computational requirements and complexity. For developers and organizations looking to implement AI solutions on edge devices or with limited resources, Small Language Models (SLMs) are emerging as a practical alternative. SLMs are not just "smaller" versions of their larger counterparts—they're designed to be faster, more efficient, and adaptable for specific tasks. With fewer parameters and lower computational needs, SLMs open the door to deploying AI on mobile devices, IoT systems, and edge environments without compromising performance. What You Stand to Learn 🧠 Introduction to Microsoft's AI Ecosystem Discover Microsoft's end-to-end AI development tools, from Azure AI Services to ONNX Runtime, enabling efficient and secure deployment of AI models across cloud and edge environments. The Advantages of SLMs over LLMs SLMs are game-changers for edge AI applications, providing faster training and inference times, reduced energy costs, and scalability across diverse devices. Hands-On with Phi-3 and ONNX Runtime Experience live demonstrations of SLMs in action with tools like Phi-3 and ONNX Runtime, showcasing how to fine-tune and deploy models on mobile devices, IoT, and hybrid cloud environments. Responsible AI Practices Understand how to safeguard your AI applications with Microsoft's Responsible AI toolkit, ensuring ethical and trustworthy deployments. Watch the Full Session 👨💻 📅 Date: December 12, 2024 ⏰ Time: 4 PM GMT | 5 PM CEST | 8 AM PT | 11 AM ET | 7 PM EAT A session packed with live demos, practical examples, and Q&A opportunities. Register NOW | Events | Microsoft Reactor Agenda 🔍 Introduction (5 min) A brief overview of the session and its focus on SLMs and LLMs. Microsoft AI Tooling (5 min) Explore the latest tools like Azure AI Services, Azure Machine Learning, and Responsible AI Tooling. How to Choose the Right Model (10 min) Key considerations such as performance, customizability, and ethical implications. Comparing SLMs vs LLMs (10 min) The strengths, weaknesses, and best use cases for both Small and Large Language Models. Deploying Models at the Edge (10 min) Insights into optimizing AI for mobile, IoT, and edge devices. Q&A Addressing participant questions about AI development and deployment.
