AI-900: Microsoft Azure AI Fundamentals Study Guide
This comprehensive study guide provides a thorough overview of the topics covered in the Microsoft Azure AI Fundamentals (AI-900) exam, including Artificial Intelligence workloads, fundamental principles of machine learning, computer vision and natural language processing workloads. Learn about the exam's intended audience, how to earn the certification, and the skills measured as of April 2022. Discover the important considerations for responsible AI, the capabilities of Azure Machine Learning Studio and more. Get ready to demonstrate your knowledge of AI and ML concepts and related Microsoft Azure services with this helpful study guide.Unleashing the Power of Model Context Protocol (MCP): A Game-Changer in AI Integration
Artificial Intelligence is evolving rapidly, and one of the most pressing challenges is enabling AI models to interact effectively with external tools, data sources, and APIs. The Model Context Protocol (MCP) solves this problem by acting as a bridge between AI models and external services, creating a standardized communication framework that enhances tool integration, accessibility, and AI reasoning capabilities. What is Model Context Protocol (MCP)? MCP is a protocol designed to enable AI models, such as Azure OpenAI models, to interact seamlessly with external tools and services. Think of MCP as a universal USB-C connector for AI, allowing language models to fetch information, interact with APIs, and execute tasks beyond their built-in knowledge. Key Features of MCP Standardized Communication – MCP provides a structured way for AI models to interact with various tools. Tool Access & Expansion – AI assistants can now utilize external tools for real-time insights. Secure & Scalable – Enables safe and scalable integration with enterprise applications. Multi-Modal Integration – Supports STDIO, SSE (Server-Sent Events), and WebSocket communication methods. MCP Architecture & How It Works MCP follows a client-server architecture that allows AI models to interact with external tools efficiently. Here’s how it works: Components of MCP MCP Host – The AI model (e.g., Azure OpenAI GPT) requesting data or actions. MCP Client – An intermediary service that forwards the AI model's requests to MCP servers. MCP Server – Lightweight applications that expose specific capabilities (APIs, databases, files, etc.). Data Sources – Various backend systems, including local storage, cloud databases, and external APIs. Data Flow in MCP The AI model sends a request (e.g., "fetch user profile data"). The MCP client forwards the request to the appropriate MCP server. The MCP server retrieves the required data from a database or API. The response is sent back to the AI model via the MCP client. Integrating MCP with Azure OpenAI Services Microsoft has integrated MCP with Azure OpenAI Services, allowing GPT models to interact with external services and fetch live data. This means AI models are no longer limited to static knowledge but can access real-time information. Benefits of Azure OpenAI Services + MCP Integration ✔ Real-time Data Fetching – AI assistants can retrieve fresh information from APIs, databases, and internal systems. ✔ Contextual AI Responses – Enhances AI responses by providing accurate, up-to-date information. ✔ Enterprise-Ready – Secure and scalable for business applications, including finance, healthcare, and retail. Hands-On Tools for MCP Implementation To implement MCP effectively, Microsoft provides two powerful tools: Semantic Workbench and AI Gateway. Microsoft Semantic Workbench A development environment for prototyping AI-powered assistants and integrating MCP-based functionalities. Features: Build and test multi-agent AI assistants. Configure settings and interactions between AI models and external tools. Supports GitHub Codespaces for cloud-based development. Explore Semantic Workbench Workbench interface examples Microsoft AI Gateway A plug-and-play interface that allows developers to experiment with MCP using Azure API Management. Features: Credential Manager – Securely handle API credentials. Live Experimentation – Test AI model interactions with external tools. Pre-built Labs – Hands-on learning for developers. Explore AI Gateway Setting Up MCP with Azure OpenAI Services Step 1: Create a Virtual Environment First, create a virtual environment using Python: python -m venv .venv Activate the environment: # Windows venv\Scripts\activate # MacOS/Linux source .venv/bin/activate Step 2: Install Required Libraries Create a requirements.txt file and add the following dependencies: langchain-mcp-adapters langgraph langchain-openai Then, install the required libraries: pip install -r requirements.txt Step 3: Set Up OpenAI API Key Ensure you have your OpenAI API key set up: # Windows setx OPENAI_API_KEY "<your_api_key> # MacOS/Linux export OPENAI_API_KEY=<your_api_key> Building an MCP Server This server performs basic mathematical operations like addition and multiplication. Create the Server File First, create a new Python file: touch math_server.py Then, implement the server: from mcp.server.fastmcp import FastMCP # Initialize the server mcp = FastMCP("Math") MCP.tool() def add(a: int, b: int) -> int: return a + b MCP.tool() def multiply(a: int, b: int) -> int: return a * b if __name__ == "__main__": mcp.run(transport="stdio") Your MCP server is now ready to run. Building an MCP Client This client connects to the MCP server and interacts with it. Create the Client File First, create a new file: touch client.py Then, implement the client: import asyncio from mcp import ClientSession, StdioServerParameters from langchain_openai import ChatOpenAI from mcp.client.stdio import stdio_client # Define server parameters server_params = StdioServerParameters( command="python", args=["math_server.py"], ) # Define the model model = ChatOpenAI(model="gpt-4o") async def run_agent(): async with stdio_client(server_params) as (read, write): async with ClientSession(read, write) as session: await session.initialize() tools = await load_mcp_tools(session) agent = create_react_agent(model, tools) agent_response = await agent.ainvoke({"messages": "what's (4 + 6) x 14?"}) return agent_response["messages"][3].content if __name__ == "__main__": result = asyncio.run(run_agent()) print(result) Your client is now set up and ready to interact with the MCP server. Running the MCP Server and Client Step 1: Start the MCP Server Open a terminal and run: python math_server.py This starts the MCP server, making it available for client connections. Step 2: Run the MCP Client In another terminal, run: python client.py Expected Output 140 This means the AI agent correctly computed (4 + 6) x 14 using both the MCP server and GPT-4o. Conclusion Integrating MCP with Azure OpenAI Services enables AI applications to securely interact with external tools, enhancing functionality beyond text-based responses. With standardized communication and improved AI capabilities, developers can build smarter and more interactive AI-powered solutions. By following this guide, you can set up an MCP server and client, unlocking the full potential of AI with structured external interactions. Next Steps: Explore more MCP tools and integrations. Extend your MCP setup to work with additional APIs. Deploy your solution in a cloud environment for broader accessibility. For further details, visit the GitHub repository for MCP integration examples and best practices. MCP GitHub Repository MCP Documentation Semantic Workbench AI Gateway MCP Video Walkthrough MCP Blog MCP Github End to End DemoAzure OpenAI Service is now generally available
Early this week Satya Nadella, Microsoft CEO, and Eric Boyd, Corporate AI Vice President, announced Azure OpenAI Service generally available, which will soon include ChatGPT – the fine-tuned version of GPT-3.5 built upon Azure AI infrastructure gone viral in the last few weeks. But let’s take a step back. What is Azure OpenAI? And how can you get started?
ChatGPT- What? Why? And How?
This blog discusses ChatGPT, a pre-trained language model that has garnered significant attention in the AI community due to its innovative capabilities. It explores the technology behind ChatGPT, its purpose, function, and utilization, as well as its potential applications and impact on the field of artificial intelligence. This blog also covers the limitations of ChatGPT and its architecture and working, including the use of advanced machine learning techniques such as Transformer and fine-tuning.How to Build an AI-Powered Developer Newsletter with Power Platform and ChatGPT3
Build a developer newsletter with Power Platform and ChatGPT3 to help developers stay up to date with the latest trends in technology. This solution will do the heavy lifting by taking advantage of the power of ChatGPT3 and Power Platform. You can generate a newsletter by following the steps outlined in this guide such as signing up for the OpenAI API, creating an adaptive card, and building a solution in Power Automate. Once the solution is established, you can run it and adjust the trigger to get the desired results. Use ChatGPT3 to generate a newsletter from the text input from Teams, and use Power Automate to send an email to the desired recipients.Mastering Azure OpenAI Services: A Comprehensive Learning Path for Aspiring AI Engineers
Are you a computer science student looking to delve into the world of Azure OpenAI Services? Look no further! In this Microsoft Learning Pathway, "Develop Generative AI solutions with Azure OpenAI Service," you'll embark on an exciting journey to harness the power of OpenAI's vast language models like ChatGPT, GPT, Codex, and Embeddings. These models are pivotal for creating innovative Natural Language Processing (NLP) solutions that can comprehend, converse, and generate content.Azure OpenAI Services in teaching and education
With the advent of Large language models(LLMs) like GPT-3, we see a transformation in education. In this article, I present my views on the future of education considering these developments. The views presented here are based on my teaching - but are a personal perspective. Today, there is a lot of excitement and speculation about GPT-3, and it is natural to ask how intelligent GPT-3 is and whether it approaches human-level intelligence. But in many ways, that's the wrong question to ask. Instead, exploring the idea of how we can build ChatGPT-like functionality using our own data is more interesting. When framed this way, we focus on the pragmatic and ignore the esoteric. If the industry adopts the 'co-pilot first approach.', educators must follow this trend to keep up with the new job roles. This will need a complete rethinking of many of the current ideas on education and the adoption of some new ideas that I proposed in this article. The conversation changes from: 'chatGPT is used for exam cheating or not' to: How can I empower my students to take up jobs of the future if the co-pilot first mode of work becomes a default?'Unlock the Future of Secure Authentication: Moving to Keyless Authentication with Managed Identity
Why Managed Identity? Traditional authentication methods often rely on keys, secrets, and passwords that can be easily compromised. Managed identity, on the other hand, provides a secure and seamless way to authenticate without the need for managing credentials. By leveraging managed identity, you can: Reduce the Risk of Compromise: As most security breaches start from identity-related issues, moving to a keyless authentication system significantly reduces the chances of such compromises. Simplify Credential Management: Managed identity eliminates the need for managing keys and secrets, making the authentication process more straightforward and less error-prone. Enhance Security: With managed identity, your applications are granted access to resources securely, without the risk of exposing sensitive credentials. Getting Started with Managed Identity To help you get started with managed identity, Microsoft offers comprehensive training modules for different programming languages. These modules cover the basics of using managed identity to authenticate to Azure OpenAI, providing you with the knowledge and skills needed to implement secure authentication in your applications. Available Microsoft Learn Training Modules: Introduction to using Managed Identity to authenticate to Azure OpenAI with .NET - Training | Microsoft Learn Introduction to Azure OpenAI Managed Identity Authentication with Java - Training | Microsoft Learn Introduction to Azure OpenAI Managed Identity Authentication with Python - Training | Microsoft Learn Introduction to Azure OpenAI Managed Identity Authentication with JavaScript - Training | Microsoft Learn Why Should Students Learn Managed Identity? As a student, learning about managed identity and keyless authentication is not just about enhancing your technical skills; it's about preparing for the future. Here are a few reasons why you should dive into managed identity: Stay Ahead in the Job Market: With cybersecurity being a top priority for organizations, having expertise in secure authentication methods like managed identity will make you a valuable asset to potential employers. Build Secure Applications: By implementing managed identity, you can build applications that are more secure, reliable, and less susceptible to breaches. Understand Modern Security Practices: Gaining knowledge about managed identity and keyless authentication will give you a deeper understanding of modern security practices and how to protect applications in today's digital landscape. Conclusion In conclusion, moving to keyless authentication through managed identity is a game-changer for securing applications. As students and future developers, embracing this technology will not only enhance your skills but also contribute to building a safer and more secure digital world. So, take the first step today by exploring the training modules and mastering the art of managed identity!Understanding the Difference in Using Different Large Language Models: Step-by-Step Guide
Unlock the secrets of deploying Large Language Models on Azure with our comprehensive guide! Learn step-by-step integration techniques for models like GPT-2, Llama 2, and Dolly v1 in your Web Applications or Power Apps. Explore detailed instructions, ready-made code, and expert tips. Join us for a live session on November 2nd, 2023, to harness the power of AI and Microsoft tools. Become an entrepreneur with Microsoft Founders Hub, offering up to $2,500 OpenAI credits and $1,000 Azure credits. Dive into the world of tech solutions and creative writing ideas today!
