The Future of AI: Customizing AI agents with the Semantic Kernel agent framework
The blog post Customizing AI agents with the Semantic Kernel agent framework discusses the capabilities of the Semantic Kernel SDK, an open-source tool developed by Microsoft for creating AI agents and multi-agent systems. It highlights the benefits of using single-purpose agents within a multi-agent system to achieve more complex workflows with improved efficiency. The Semantic Kernel SDK offers features like telemetry, hooks, and filters to ensure secure and responsible AI solutions, making it a versatile tool for both simple and complex AI projects.Create your own QA RAG Chatbot with LangChain.js + Azure OpenAI Service
Demo: Mpesa for Business Setup QA RAG Application In this tutorial we are going to build a Question-Answering RAG Chat Web App. We utilize Node.js and HTML, CSS, JS. We also incorporate Langchain.js + Azure OpenAI + MongoDB Vector Store (MongoDB Search Index). Get a quick look below. Note: Documents and illustrations shared here are for demo purposes only and Microsoft or its products are not part of Mpesa. The content demonstrated here should be used for educational purposes only. Additionally, all views shared here are solely mine. What you will need: An active Azure subscription, get Azure for Student for free or get started with Azure for 12 months free. VS Code Basic knowledge in JavaScript (not a must) Access to Azure OpenAI, click here if you don't have access. Create a MongoDB account (You can also use Azure Cosmos DB vector store) Setting Up the Project In order to build this project, you will have to fork this repository and clone it. GitHub Repository link: https://github.com/tiprock-network/azure-qa-rag-mpesa . Follow the steps highlighted in the README.md to setup the project under Setting Up the Node.js Application. Create Resources that you Need In order to do this, you will need to have Azure CLI or Azure Developer CLI installed in your computer. Go ahead and follow the steps indicated in the README.md to create Azure resources under Azure Resources Set Up with Azure CLI. You might want to use Azure CLI to login in differently use a code. Here's how you can do this. Instead of using az login. You can do az login --use-code-device OR you would prefer using Azure Developer CLI and execute this command instead azd auth login --use-device-code Remember to update the .env file with the values you have used to name Azure OpenAI instance, Azure models and even the API Keys you have obtained while creating your resources. Setting Up MongoDB After accessing you MongoDB account get the URI link to your database and add it to the .env file along with your database name and vector store collection name you specified while creating your indexes for a vector search. Running the Project In order to run this Node.js project you will need to start the project using the following command. npm run dev The Vector Store The vector store used in this project is MongoDB store where the word embeddings were stored in MongoDB. From the embeddings model instance we created on Azure AI Foundry we are able to create embeddings that can be stored in a vector store. The following code below shows our embeddings model instance. //create new embedding model instance const azOpenEmbedding = new AzureOpenAIEmbeddings({ azureADTokenProvider, azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, azureOpenAIApiEmbeddingsDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_EMBEDDING_NAME, azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION, azureOpenAIBasePath: "https://eastus2.api.cognitive.microsoft.com/openai/deployments" }); The code in uploadDoc.js offers a simple way to do embeddings and store them to MongoDB. In this approach the text from the documents is loaded using the PDFLoader from Langchain community. The following code demonstrates how the embeddings are stored in the vector store. // Call the function and handle the result with await const storeToCosmosVectorStore = async () => { try { const documents = await returnSplittedContent() //create store instance const store = await MongoDBAtlasVectorSearch.fromDocuments( documents, azOpenEmbedding, { collection: vectorCollection, indexName: "myrag_index", textKey: "text", embeddingKey: "embedding", } ) if(!store){ console.log('Something wrong happened while creating store or getting store!') return false } console.log('Done creating/getting and uploading to store.') return true } catch (e) { console.log(`This error occurred: ${e}`) return false } } In this setup, Question Answering (QA) is achieved by integrating Azure OpenAI’s GPT-4o with MongoDB Vector Search through LangChain.js. The system processes user queries via an LLM (Large Language Model), which retrieves relevant information from a vectorized database, ensuring contextual and accurate responses. Azure OpenAI Embeddings convert text into dense vector representations, enabling semantic search within MongoDB. The LangChain RunnableSequence structures the retrieval and response generation workflow, while the StringOutputParser ensures proper text formatting. The most relevant code snippets to include are: AzureChatOpenAI instantiation, MongoDB connection setup, and the API endpoint handling QA queries using vector search and embeddings. There are some code snippets below to explain major parts of the code. Azure AI Chat Completion Model This is the model used in this implementation of RAG, where we use it as the model for chat completion. Below is a code snippet for it. const llm = new AzureChatOpenAI({ azTokenProvider, azureOpenAIApiInstanceName: process.env.AZURE_OPENAI_API_INSTANCE_NAME, azureOpenAIApiDeploymentName: process.env.AZURE_OPENAI_API_DEPLOYMENT_NAME, azureOpenAIApiVersion: process.env.AZURE_OPENAI_API_VERSION }) Using a Runnable Sequence to give out Chat Output This shows how a runnable sequence can be used to give out a response given the particular output format/ output parser added on to the chain. //Stream response app.post(`${process.env.BASE_URL}/az-openai/runnable-sequence/stream/chat`, async (req,res) => { //check for human message const { chatMsg } = req.body if(!chatMsg) return res.status(201).json({ message:'Hey, you didn\'t send anything.' }) //put the code in an error-handler try{ //create a prompt template format template const prompt = ChatPromptTemplate.fromMessages( [ ["system", `You are a French-to-English translator that detects if a message isn't in French. If it's not, you respond, "This is not French." Otherwise, you translate it to English.`], ["human", `${chatMsg}`] ] ) //runnable chain const chain = RunnableSequence.from([prompt, llm, outPutParser]) //chain result let result_stream = await chain.stream() //set response headers res.setHeader('Content-Type','application/json') res.setHeader('Transfer-Encoding','chunked') //create readable stream const readable = Readable.from(result_stream) res.status(201).write(`{"message": "Successful translation.", "response": "`); readable.on('data', (chunk) => { // Convert chunk to string and write it res.write(`${chunk}`); }); readable.on('end', () => { // Close the JSON response properly res.write('" }'); res.end(); }); readable.on('error', (err) => { console.error("Stream error:", err); res.status(500).json({ message: "Translation failed.", error: err.message }); }); }catch(e){ //deliver a 500 error response return res.status(500).json( { message:'Failed to send request.', error:e } ) } }) To run the front end of the code, go to your BASE_URL with the port given. This enables you to run the chatbot above and achieve similar results. The chatbot is basically HTML+CSS+JS. Where JavaScript is mainly used with fetch API to get a response. Thanks for reading. I hope you play around with the code and learn some new things. Additional Reads Introduction to LangChain.js Create an FAQ Bot on Azure Build a basic chat app in Python using Azure AI Foundry SDKExploring 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-beginnersStep-by-step: Integrate Ollama Web UI to use Azure Open AI API with LiteLLM Proxy
Introductions Ollama WebUI is a streamlined interface for deploying and interacting with open-source large language models (LLMs) like Llama 3 and Mistral, enabling users to manage models, test them via a ChatGPT-like chat environment, and integrate them into applications through Ollama’s local API. While it excels for self-hosted models on platforms like Azure VMs, it does not natively support Azure OpenAI API endpoints—OpenAI’s proprietary models (e.g., GPT-4) remain accessible only through OpenAI’s managed API. However, tools like LiteLLM bridge this gap, allowing developers to combine Ollama-hosted models with OpenAI’s API in hybrid workflows, while maintaining compliance and cost-efficiency. This setup empowers users to leverage both self-managed open-source models and cloud-based AI services. Problem Statement As of February 2025, Ollama WebUI, still do not support Azure Open AI API. The Ollama Web UI only support self-hosted Ollama API and managed OpenAI API service (PaaS). This will be an issue if users want to use Open AI models they already deployed on Azure AI Foundry. Objective To integrate Azure OpenAI API via LiteLLM proxy into with Ollama Web UI. LiteLLM translates Azure AI API requests into OpenAI-style requests on Ollama Web UI allowing users to use OpenAI models deployed on Azure AI Foundry. If you haven’t hosted Ollama WebUI already, follow my other step-by-step guide to host Ollama WebUI on Azure. Proceed to the next step if you have Ollama WebUI deployed already. Step 1: Deploy OpenAI models on Azure Foundry. If you haven’t created an Azure AI Hub already, search for Azure AI Foundry on Azure, and click on the “+ Create” button > Hub. Fill out all the empty fields with the appropriate configuration and click on “Create”. After the Azure AI Hub is successfully deployed, click on the deployed resources and launch the Azure AI Foundry service. To deploy new models on Azure AI Foundry, find the “Models + Endpoints” section on the left hand side and click on “+ Deploy Model” button > “Deploy base model” A popup will appear, and you can choose which models to deploy on Azure AI Foundry. Please note that the o-series models are only available to select customers at the moment. You can request access to the o-series models by completing this request access form, and wait until Microsoft approves the access request. Click on “Confirm” and another popup will emerge. Now name the deployment and click on “Deploy” to deploy the model. Wait a few moments for the model to deploy. Once it successfully deployed, please save the “Target URI” and the API Key. Step 2: Deploy LiteLLM Proxy via Docker Container Before pulling the LiteLLM Image into the host environment, create a file named “litellm_config.yaml” and list down the models you deployed on Azure AI Foundry, along with the API endpoints and keys. Replace "API_Endpoint" and "API_Key" with “Target URI” and “Key” found from Azure AI Foundry respectively. Template for the “litellm_config.yaml” file. model_list: - model_name: [model_name] litellm_params: model: azure/[model_name_on_azure] api_base: "[API_ENDPOINT/Target_URI]" api_key: "[API_Key]" api_version: "[API_Version]" Tips: You can find the API version info at the end of the Target URI of the model's endpoint: Sample Endpoint - https://example.openai.azure.com/openai/deployments/o1-mini/chat/completions?api-version=2024-08-01-preview Run the docker command below to start LiteLLM Proxy with the correct settings: docker run -d \ -v $(pwd)/litellm_config.yaml:/app/config.yaml \ -p 4000:4000 \ --name litellm-proxy-v1 \ --restart always \ ghcr.io/berriai/litellm:main-latest \ --config /app/config.yaml --detailed_debug Make sure to run the docker command inside the directory where you created the “litellm_config.yaml” file just now. The port used to listen for LiteLLM Proxy traffic is port 4000. Now that LiteLLM proxy had been deployed on port 4000, lets change the OpenAI API settings on Ollama WebUI. Navigate to Ollama WebUI’s Admin Panel settings > Settings > Connections > Under the OpenAI API section, write http://127.0.0.1:4000 as the API endpoint and set any key (You must write anything to make it work!). Click on “Save” button to reflect the changes. Refresh the browser and you should be able to see the AI models deployed on the Azure AI Foundry listed in the Ollama WebUI. Now let’s test the chat completion + Web Search capability using the "o1-mini" model on Ollama WebUI. Conclusion Hosting Ollama WebUI on an Azure VM and integrating it with OpenAI’s API via LiteLLM offers a powerful, flexible approach to AI deployment, combining the cost-efficiency of open-source models with the advanced capabilities of managed cloud services. While Ollama itself doesn’t support Azure OpenAI endpoints, the hybrid architecture empowers IT teams to balance data privacy (via self-hosted models on Azure AI Foundry) and cutting-edge performance (using Azure OpenAI API), all within Azure’s scalable ecosystem. This guide covers every step required to deploy your OpenAI models on Azure AI Foundry, set up the required resources, deploy LiteLLM Proxy on your host machine and configure Ollama WebUI to support Azure AI endpoints. You can test and improve your AI model even more with the Ollama WebUI interface with Web Search, Text-to-Image Generation, etc. all in one place.Deploy Open Web UI on Azure VM via Docker: A Step-by-Step Guide with Custom Domain Setup.
Introductions Open Web UI (often referred to as "Ollama Web UI" in the context of LLM frameworks like Ollama) is an open-source, self-hostable interface designed to simplify interactions with large language models (LLMs) such as GPT-4, Llama 3, Mistral, and others. It provides a user-friendly, browser-based environment for deploying, managing, and experimenting with AI models, making advanced language model capabilities accessible to developers, researchers, and enthusiasts without requiring deep technical expertise. This article will delve into the step-by-step configurations on hosting OpenWeb UI on Azure. Requirements: Azure Portal Account - For students you can claim $USD100 Azure Cloud credits from this URL. Azure Virtual Machine - with a Linux of any distributions installed. Domain Name and Domain Host Caddy Open WebUI Image Step One: Deploy a Linux – Ubuntu VM from Azure Portal Search and Click on “Virtual Machine” on the Azure portal search bar and create a new VM by clicking on the “+ Create” button > “Azure Virtual Machine”. Fill out the form and select any Linux Distribution image – In this demo, we will deploy Open WebUI on Ubuntu Pro 24.04. Click “Review + Create” > “Create” to create the Virtual Machine. Tips: If you plan to locally download and host open source AI models via Open on your VM, you could save time by increasing the size of the OS disk / attach a large disk to the VM. You may also need a higher performance VM specification since large resources are needed to run the Large Language Model (LLM) locally. Once the VM has been successfully created, click on the “Go to resource” button. You will be redirected to the VM’s overview page. Jot down the public IP Address and access the VM using the ssh credentials you have setup just now. Step Two: Deploy the Open WebUI on the VM via Docker Once you are logged into the VM via SSH, run the Docker Command below: docker run -d --name open-webui --network=host --add-host=host.docker.internal:host-gateway -e PORT=8080 -v open-webui:/app/backend/data --restart always ghcr.io/open-webui/open-webui:dev This Docker command will download the Open WebUI Image into the VM and will listen for Open Web UI traffic on port 8080. Wait for a few minutes and the Web UI should be up and running. If you had setup an inbound Network Security Group on Azure to allow port 8080 on your VM from the public Internet, you can access them by typing into the browser: [PUBLIC_IP_ADDRESS]:8080 Step Three: Setup custom domain using Caddy Now, we can setup a reverse proxy to map a custom domain to [PUBLIC_IP_ADDRESS]:8080 using Caddy. The reason why Caddy is useful here is because they provide automated HTTPS solutions – you don’t have to worry about expiring SSL certificate anymore, and it’s free! You must download all Caddy’s dependencies and set up the requirements to install it using this command: sudo apt install -y debian-keyring debian-archive-keyring apt-transport-https curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/gpg.key' | sudo gpg --dearmor -o /usr/share/keyrings/caddy-stable-archive-keyring.gpg curl -1sLf 'https://dl.cloudsmith.io/public/caddy/stable/debian.deb.txt' | sudo tee /etc/apt/sources.list.d/caddy-stable.list sudo apt update && sudo apt install caddy Once Caddy is installed, edit Caddy’s configuration file at: /etc/caddy/Caddyfile , delete everything else in the file and add the following lines: yourdomainname.com { reverse_proxy localhost:8080 } Restart Caddy using this command: sudo systemctl restart caddy Next, create an A record on your DNS Host and point them to the public IP of the server. Step Four: Update the Network Security Group (NSG) To allow public access into the VM via HTTPS, you need to ensure the NSG/Firewall of the VM allow for port 80 and 443. Let’s add these rules into Azure by heading to the VM resources page you created for Open WebUI. Under the “Networking” Section > “Network Settings” > “+ Create port rule” > “Inbound port rule” On the “Destination port ranges” field, type in 443 and Click “Add”. Repeat these steps with port 80. Additionally, to enhance security, you should avoid external users from directly interacting with Open Web UI’s port - port 8080. You should add an inbound deny rule to that port. With that, you should be able to access the Open Web UI from the domain name you setup earlier. Conclusion And just like that, you’ve turned a blank Azure VM into a sleek, secure home for your Open Web UI, no magic required! By combining Docker’s simplicity with Caddy’s “set it and forget it” HTTPS magic, you’ve not only made your app accessible via a custom domain but also locked down security by closing off risky ports and keeping traffic encrypted. Azure’s cloud muscle handles the heavy lifting, while you get to enjoy the perks of a pro setup without the headache. If you are interested in using AI models deployed on Azure AI Foundry on OpenWeb UI via API, kindly read my other article: Step-by-step: Integrate Ollama Web UI to use Azure Open AI API with LiteLLM ProxyThe Future of AI: Reduce AI Provisioning Effort - Jumpstart your solutions with AI App Templates
In the previous post, we introduced Contoso Chat – an open-source RAG-based retail chat sample for Azure AI Foundry, that serves as both an AI App template (for builders) and the basis for a hands-on workshop (for learners). And we briefly talked about five stages in the developer workflow (provision, setup, ideate, evaluate, deploy) that take them from the initial prompt to a deployed product. But how can that sample help you build your app? The answer lies in developer tools and AI App templates that jumpstart productivity by giving you a fast start and a solid foundation to build on. In this post, we answer that question with a closer look at Azure AI App templates - what they are, and how we can jumpstart our productivity with a reuse-and-extend approach that builds on open-source samples for core application architectures.The Future of AI: Harnessing AI for E-commerce - personalized shopping agents
Explore the development of personalized shopping agents that enhance user experience by providing tailored product recommendations based on uploaded images. Leveraging Azure AI Foundry, these agents analyze images for apparel recognition and generate intelligent product recommendations, creating a seamless and intuitive shopping experience for retail customers.
Empower Your Nonprofit with AI: A Beginner’s Guide to Teaching AI Skills
That’s where AI Skills for Educators comes in—a free resource designed to help educators (including nonprofit leaders, trainers, and community advocates) introduce AI concepts to their audience. Why Should Nonprofits Teach AI? Nonprofits thrive on impact. By integrating AI education into your programs, you can: ✅ Expand career opportunities for your community members. ✅ Boost digital literacy and future-proof skills. ✅ Enhance nonprofit operations by leveraging AI for efficiency. Whether you're running workforce development programs, youth education initiatives, or volunteer training, AI skills can add immense value. What Is AI Skills for Educators? Microsoft’s AI Skills for Educators is a free training hub that provides lesson plans, resources, and interactive learning materials to help you teach AI—even if you’re not an AI expert. Key Features: 🎓 AI Basics – Understand and teach foundational AI concepts. 📖 Pre-made Lesson Plans – Save time with structured AI courses. 🖥️ Hands-on Exercises – Engage learners with real-world AI scenarios. 🎤 Presentation Materials – Ready-to-use slides and guides for training. How Can Your Nonprofit Use These Resources? Here are some ways your nonprofit can start teaching AI today: 1. AI Workshops for Your Community Use the free lesson plans to host in-person or virtual AI workshops. Whether it's a basic AI introduction for job seekers or advanced concepts for tech learners, these resources provide an easy starting point. 2. AI Training for Volunteers & Staff Empower your internal team with AI knowledge! Whether it's automating administrative tasks or enhancing outreach, an AI-skilled team can work smarter. 3. Partner with Schools & Libraries Collaborate with educators, libraries, and youth programs to introduce AI concepts to students and underrepresented communities. AI education shouldn't be limited to tech companies—your nonprofit can bridge the gap. 4. Digital Equity & Inclusion Programs If your nonprofit focuses on closing the digital divide, these resources can help you teach AI literacy in underserved communities, giving more people access to future job opportunities. Getting Started Is Easy! ✅ Step 1: Visit aka.ms/AISkillsForEducators. ✅ Step 2: Explore the lesson plans and materials. ✅ Step 3: Adapt the content to fit your nonprofit’s mission. ✅ Step 4: Start teaching AI in your community! By integrating AI education into your nonprofit, you're not just teaching technology—you’re shaping futures. 💡 Is your nonprofit ready to empower communities with AI? Visit aka.ms/AISkillsForEducators and start making an impact today!The Future of AI: Power Your Agents with Azure Logic Apps
Building intelligent applications no longer requires complex coding. With advancements in technology, you can now create agents using cloud-based tools to automate workflows, connect to various services, and integrate business processes across hybrid environments without writing any code.Automate Quota Discovery in Azure AI Foundry: A Tale of 3 APIs
Automate the discovery of Azure regions that meet your AI deployment needs using three essential APIs: Models API, Usages API, and Locations API. This process helps reduce decision fatigue and ensures compliance with enterprise-wide model deployment standards. Key learnings: Model Deployment Requirements: Understand the needs of a standard Retrieval-Augmented Generation (RAG) application, which involves deploying multiple models. Automation Benefits: Streamline your deployment process and ensure compliance with enterprise standards. Three Essential APIs: Models API: Query available models for a specific subscription within a chosen location. Usages API: Assess current usages and limits to infer available quotas. Locations API: Obtain a list of all available regions. A comprehensive Jupyter notebook with the implementation steps is available in the accompanying GitHub repository. This resource is invaluable for AI developers looking to streamline their deployment processes and ensure their applications meet all necessary requirements
