Build, Deploy, & Manage AI with Azure AI Foundry
Microsoft's Unified AI Development Platform Imagine an Enterprise organization with multiple departments which need to create new AI solutions to streamline operations while boosting customer experience. Each has different objectives and goals they are trying to achieve with AI. Marketing wants to analyze customer engagement on social media, Finance aims to spot fraud, and Operations plans to predict when machines need repairs. Teams have different subscriptions, resource groups, storage, etc. per department. Resource management can be tedious to say the least while sharing data safely, with the added complexity of provisioning things accurately. That is where Azure AI Foundry comes in. Azure AI Foundry is a unified platform allowing organizations to have a centralized hub where they can manage their AI development with the tools and features they need. Nonprofits can now step into the world of AI and build their own solutions for their organization and the communities they serve. Azure AI Foundry is accessible to developers and beginners alike, making AI implementation cost-effective for organizations of any size. In this blog we will cover How you can get started with Azure AI Foundry. Before we begin, there are some prerequisites that need to be made before you start your journey. Prerequisites & Azure Role Based Access Control (RBAC) Acquiring an Azure Account Azure AI Foundry is integrated into Microsoft’s Azure cloud infrastructure. To use the platform, you will need an Azure Account. You need to be assigned the role of Owner or have your administrator assign you the appropriate role. You can learn more about Azure AI Foundry roles in the Role Comparison Between Foundry Projects and Hub Based Projects. Nonprofits can take advantage of Microsoft’s Nonprofit $2000 Azure Sponsorship Credit Subscription. You will need to be an approved participant of Microsoft’s Nonprofit Offers Program. To learn more about how you can get started please see the following blogs: Getting Signed Up with Microsoft Nonprofits Program | Microsoft Community Hub Claiming Azure Credits | Microsoft Community Hub Azure Role Based Access Control (RBAC) Access Control and identity management are crucial steps in safeguarding your sensitive data. Organizations that deal with global privacy compliance standards understand the necessity of securing and hardening their environment. Microsoft aims to empower clients with security tools and measures built in Azure to help secure access to their resources. One of these tools is Microsoft Entra ID (formerly known as Azure Active Directory) which applies built-in roles with limited access and permissions to resources based on their job function, known as Role Based Access Control (RBAC). This follows a security principle called The Principle of Least Privilege. For example, a Business Analyst may need access to Customer Relationship Management software (CRM) to record interactions with stakeholders, allocate budgets, and manage financial records. The Business Analyst would need administrative access related to worked performed. However, they would not need access to creating resources such as virtual machines since that is out of the scope of their role. This ensures security best practices to prevent access to highly sensitive data. Azure AI Foundry has roles designed for developers, managers, and users. By assigning specific roles, such as reader or manager, organizations can ensure that only authorized individuals can view or modify critical AI tools and data. Keep this in mind when granting access to users. Below is a comparison of the features and capabilities of the two project types within Azure AI Foundry: Foundry Project and Hub Based Project. Disclaimer: Some roles may limit functionality in the Azure AI Foundry portal. For example, if a user cannot create a compute instance, that option will not appear in the studio. This prevents access denied errors. Types of Projects Foundry Project Hub-based Project Built on Azure AI Foundry resource Agents Azure AI Foundry Models Azure AI Foundry API Agents Project files (upload and start experimenting) Project-level isolation of files and outputs Evaluations Playground Hosted on Azure AI Foundry Hub Agents (preview) Create if features are not available in Foundry project Azure AI Foundry Models (Connections) Azure AI Foundry API Agents (Connections) Project-level isolation of files and outputs Evaluations Playground Prompt flow Managed compute Azure Storage account & Azure Key Vault Role Comparison Between Foundry Project & Hub Based Project Foundry Project Azure AI User: Azure AI User This role grants reader access to AI projects, reader access to AI accounts, and data actions for an AI project. This role is automatically assigned to the user if they can assign roles. If not, this role must be granted by your subscription Owner or user with role assignment privileges. Azure AI Project Manager: Azure AI Project Manager This role lets you perform management actions on Azure AI Foundry projects, build and develop projects, and grants conditional assignment of the Azure AI User role to other user principles. Azure AI Account Owner: Azure AI Account Owner This role grants full access to managing AI projects, accounts, and grants conditional assignment of the Azure AI User role to other user principles. Hub-Based Project Owner: Full access to the hub, including the ability to manage and create new hubs and assign permissions. This role is automatically assigned to the hub creator Contributor: Users have full access to the hub, including the ability to create new hubs, but cannot manage hub permissions on the existing resource. Azure AI Administrator (preview): This role is automatically assigned to the system-assigned managed identity for the hub. The Azure AI Administrator role has the minimum permissions needed for the managed identity to perform its tasks. For more information, see Azure AI Administrator role (preview). Azure AI Developer: Perform all actions except create new hubs and manage the hub permissions. For example, users can create projects, compute, and connections. Users can assign permissions within their project. Users can interact with existing Azure AI resources such as Azure OpenAI, Azure AI Search, and Azure AI services. Azure AI Inference Deployment Operator: Perform all actions required to create a resource deployment within a resource group. Reader: Read only access to the hub. This role is automatically assigned to all project members within the hub. Playgrounds, Agents, & Models Oh My! Model Catalog Investing in AI can be expensive, from overhead to capital expenditure. Adoption and development can be costly for many organizations with tight budgets. Nonprofits that want to venture in AI development are met with the challenge of balancing budget with performance and navigating the ever-evolving AI landscape. Nonprofits need the ability to evaluate and test drive models before making the major investment to develop AI projects. Azure AI Foundry now makes it easy to compare models and benchmarks for the latest AI models. Choose from a comprehensive collection of models from Open AI, Meta, Mistral, Grok, Cohere, and more. Track your model's quota usage to stay within limits. Fine-tuned AI Models Create tailored experiences with fine-tuned AI models by utilizing base models from Azure AI Foundry and adapting your own data to create an experience to cater to your audience. For nonprofits and businesses alike, fine-tuned models offer a practical path to maximize impact without the need for intensive computational resources or expertise. Whether optimizing for customer support, document summarization, healthcare analysis, or content generation, fine-tuning ensures AI solutions are more effective and aligned to user needs. Playgrounds Playgrounds are a workspace where you can work on GPTs, Assistants, Real-time audio, Images, and Completions. Playgrounds are a great way to test and compare models before making a full commitment to adopting them. Built-in tools let you quickly benchmark and evaluate what works best with your needs. You can choose from a variety of the latest models from OpenAI and third-party vendors. Setup is made simple with just a few clicks by picking your model. Chat: A chat playground lets users work with AI chat models in real time. Assistants: The Assistants’ playground is designed for experimenting with AI-driven assistants tailored to a wide range of tasks. Real-time audio: The Real-time Audio playground provides an interactive space to experiment with advanced audio-based AI models. Images: The Images playground offers an intuitive environment for working with state-of-the-art image generation and analysis models. Completions: The Completions playground allows users to test text generation models by providing prompts and adjusting settings for tasks such as content creation, summarization, or code generation. As you can see, you have many options to choose from. Create agentic bots for customer interactions or develop a chatbot for end users using specific organizational knowledge such as FAQs and documents with citations. The sky is the limit, with Azure adding new features and capabilities to improve user experience. Developers can also get started with templates and use IDEs like Visual Studio and Visual Studio Code. Now, let us talk about how you can integrate your data to refine and improve your workflows. In the next section we will discuss how you can connect your data to your customized solutions. Connecting Data Sources Connecting your data storage to Azure AI Foundry’s playground assistants, fine-tuned models, batch pipelines, and evaluation workflows is direct and straightforward. You can link storage accounts, databases, Azure blob storage, uploaded files, and Azure AI Search to supply datasets for training, testing, or real-time use. Built-in connectors and APIs make integration simple, while role-based permissions control access. Data lineage and versioning help track and manage information, ensuring your assistants and models use accurate, reliable inputs before applying additional security and governance tools. Compatible Storage Types Azure Blob Storage Azure AI Search Azure Cosmo DB for Mongo DB Uploaded Files URL/ Web Address JSON Governance & Security Azure AI Foundry provides tools to ensure the security of projects. One such tool is Role Based Access Control (RBAC), which we spoke about early. However, Azure AI Foundry integrates a security framework designed to protect sensitive data and comply with industry standards. It employs a combination of tools, governance controls, and continuous monitoring to assist organizations in developing AI solutions securely. Users can set up controls like content filters and block lists. Security recommendations are available through Windows Defender XDR integration, offering protection against data leakage, data poisoning, jailbreaks, and credential theft. Additionally, compliance policies from Microsoft Purview help maintain security measures. Security & Governance Features Compliance Security Framework Private Endpoints & Network Isolation Role Based Access Control Guard rails & Controls Data Encryption Microsoft Purview Defender XDR Integration Taken together, robust governance and security features offer organizations peace of mind, ensuring that their AI projects are not only innovative but also responsibly managed and protected against emerging threats. As organizations scale their AI initiatives, understanding and managing resource usage becomes equally important. This is where quotas come into play, helping teams allocate resources efficiently and maintain optimal performance as they build and deploy AI solutions. Managing Token Quotas Azure AI Foundry provides comprehensive tools that empower teams to monitor and manage token quotas across a diverse range of model consumption patterns. Whether your workloads are categorized as Global standard, Global provisioned, Global batch, Data zone standard, Data zone provisioned, Data zone batch, standard, or Regional, the platform allows for granular visibility into resource allocation and consumption. This centralized tracking ensures that organizations can proactively identify usage bottlenecks, optimize deployment strategies, and stay within defined limits, all while supporting efficient scaling and sustaining high performance for their AI solutions. How to Get Started Get started by visiting Azure AI foundry at https://ai.azure.com. Begin leveraging Azure AI Foundry, organizations should first explore the platform’s intuitive interface and robust documentation, which offer step-by-step guidance for onboarding teams of any size. Users can discover a suite of developer SDKs, prebuilt templates, and ready-to-deploy chatbot solutions that expedite the setup process. Engaging with these resources enables teams to rapidly prototype, customize, and scale AI solutions according to their unique requirements. Additionally, organizations are encouraged to take advantage of the extensive educational content and support channels provided, ensuring a smooth transition from initial exploration to full-scale AI deployment. With these tools and resources at their fingertips, teams can confidently embark on their AI journey, transforming innovative ideas into impactful outcomes. Hyperlinks Introducing Azure AI Foundry - Everything you need for AI development Build your own copilot with Azure AI Studio (Part 1) | Microsoft Learn Role-based access control in Azure AI Foundry portal - Azure AI Foundry | Microsoft Learn QuickStart: Get started with Azure AI Foundry - Azure AI Foundry | Microsoft Learn How to configure a private link for an Azure AI Foundry hub - Azure AI Foundry | Microsoft Learn Azure OpenAI Service - Pricing | Microsoft AzureUnderstanding the Fundamentals of AI Concepts for Nonprofits
Artificial Intelligence (AI) has become a cornerstone of modern technology, driving innovation across various sectors. Nonprofits, too, can harness the power of AI to enhance their operations and amplify their impact. In this blog, we'll explore fundamental AI concepts, common AI workloads, Microsoft's Responsible AI policies, and the tools and services available through Azure AI, all tailored for the nonprofit sector. Understanding AI Workloads AI workloads refer to the different types of tasks that AI systems can perform. Here are some common AI workloads relevant to nonprofits: Machine Learning: This involves training a computer model to make predictions and draw conclusions from data. Nonprofits can use machine learning to predict donor behavior, optimize fundraising strategies, and analyze program outcomes. Computer Vision: This capability allows software to interpret the world visually through cameras, video, and images. Applications include identifying and tracking wildlife for conservation efforts or analyzing images to assess disaster damage. Natural Language Processing (NLP): NLP enables computers to understand and respond to human language. Nonprofits can use NLP for sentiment analysis of social media posts, language translation for multilingual communities, and developing conversational AI like chatbots for donor engagement. Anomaly Detection: This involves automatically detecting errors or unusual activity. It is useful for fraud detection in financial transactions, monitoring network security, and ensuring data integrity. Conversational AI: This refers to the capability of a software agent to engage in conversations with humans. Examples include chatbots and virtual assistants that can answer questions, provide recommendations, and perform tasks, enhancing donor and beneficiary interactions. Responsible AI Practices As AI technology continues to evolve, it is crucial to ensure it is developed and used responsibly. Microsoft's Responsible AI policies emphasize the importance of fairness, reliability, safety, privacy, security, inclusiveness, transparency, and accountability in AI systems. These principles guide the development and deployment of AI solutions to ensure they benefit everyone and do not cause harm. To learn more about Microsoft Responsible AI Practices click here: Empowering responsible AI practices | Microsoft AI Azure AI Services for Nonprofits Microsoft Azure offers a suite of AI services that enable nonprofits to build intelligent applications. Some key services include: Azure Machine Learning: A comprehensive platform for building, training, and deploying machine learning models. It supports a wide range of machine learning frameworks and tools, helping nonprofits analyze data and make informed decisions. To learn more or get started with Azure Machine Learning click here: Azure Machine Learning - ML as a Service | Microsoft Azure Azure AI Bot Service: A service for building conversational AI applications. It provides tools for creating, testing, and deploying chatbots that can interact with users through various channels, improving donor engagement and support services. To learn more or get started with Azure AI Bot Service click here: Azure AI Bot Service | Microsoft Azure Azure Cognitive Services: A collection of APIs that enable developers to add AI capabilities to their applications. These services include vision, speech, language, and decision-making APIs, which can be used for tasks like image recognition, language translation, and sentiment analysis. To learn more about the various Cognitive Service please click here: Azure AI Services – Using AI for Intelligent Apps | Microsoft Azure Conclusion AI has the potential to transform the nonprofit sector by enhancing efficiency, driving innovation, and providing valuable insights. By understanding AI workloads, adhering to responsible AI practices, and leveraging Azure AI services, nonprofits can unlock the full potential of AI to better serve their communities and achieve their missions. Embrace the power of AI to take your nonprofit organization to new heights and make a greater impact. For a deeper dive into the fundamental concepts of AI, please visit the module Fundamental AI Concepts. This resource will provide you with essential insights and a solid foundation to enhance your knowledge in the ever-evolving field of artificial intelligence.Harmonizing Creativity: AI-Powered Music Making with Copilot and Suno
In the ever-evolving world of music production, technology has opened doors that once seemed closed. The future is now, and it’s powered by AI tools that make the songwriting and melody-making process more accessible, innovative, and, let’s be honest, a whole lot of fun! Two such groundbreaking tools—Copilot and Suno AI—are revolutionizing the way we create music. Whether you’re a seasoned musician or just dipping your toes into songwriting, these tools are about to change the game for you. The Power of AI in Songwriting Songwriting can sometimes feel like a battle with your own brain—chasing after that perfect phrase, that killer melody, only to find yourself stuck in a loop of frustration. Here’s where AI steps in. Copilot and Suno AI aren’t just about automating tasks—they are about enhancing your creativity, helping you tap into new sources of inspiration, and pushing your music to places you might never have gone on your own. Copilot: The Lyric Writing Partner You’ve Been Waiting For Copilot is a tool that acts like an extension of your creative mind. Think of it like a songwriting buddy who never runs out of ideas, can offer fresh perspectives, and isn’t afraid to suggest bold ideas. Whether you're stuck on the first line of a song or looking for a catchy chorus, Copilot can help. Here’s how it works: Brainstorming Ideas: If you're looking to write lyrics about a specific theme or emotion, just input a few keywords, and Copilot will help generate lyrical suggestions. It’s like having a starting point when you’re feeling creatively dry. Building Structure: Sometimes, knowing how to start is half the battle. Copilot can suggest various song structures, from verses to choruses, bridges to intros. Need a fresh perspective on how to build your song? Copilot has you covered. Wordplay and Rhymes: For the wordsmiths out there, Copilot’s ability to play with words and rhyme schemes is an invaluable tool. You can experiment with different lyrics and find the perfect word combinations you wouldn’t have thought of yourself. The magic of Copilot isn’t that it just writes lyrics for you—it pushes you in new directions, offering suggestions that fuel your own creativity, letting you fine-tune and shape your ideas in real-time. Suno AI (Third-Party Tool): The Melody Maker While Copilot handles your lyrics, Suno AI is your musical partner, capable of crafting melodies that align with your lyrics, setting the perfect mood for your song. Suno specializes in melody generation, so if you're stuck at the piano or guitar, unsure how to bring your lyrics to life, Suno can suggest harmonic progressions and melodic lines that enhance the emotional weight of your song. Imagine trying to write a soulful ballad but feeling disconnected from the right melody. With Suno AI, you input your lyrics and the emotional tone you want to convey, and it generates musical ideas that match that vibe. Whether you're aiming for a catchy pop hook, a somber indie tune, or something experimental, Suno adapts to your needs. Suno AI also allows for customization. You can refine the generated melody, adjusting it to fit the mood you’re trying to create. The beauty of Suno lies in how it bridges the gap between theoretical music knowledge and practical creation, offering melody ideas that might take a human composer hours to create. How Copilot and Suno Work Together: The Dream Team Now, imagine combining Copilot and Suno AI in one seamless workflow. You start by crafting your lyrics with Copilot, brainstorming themes or digging into emotional topics. Once you have the lyrics, you turn to Suno AI, inputting the mood and vibe you want for the melody. Here’s an example: You’ve written lyrics about love, inspired by a joyful moment you shared with someone special. Copilot has helped you perfect the words, but something’s still missing. You need a melody that captures the excitement and warmth in your lyrics. You turn to Suno, telling it you want an upbeat, cheerful melody. In an instant, Suno creates a bright, lively piano progression that fills your lyrics with energy and joy, bringing the words to life with an infectious sense of happiness. The result? A fully formed song that feels both emotionally authentic and musically cohesive, all thanks to the synergy between AI tools. How to Get Started: To start with the lyrics: Go to copilot.microsoft.com Choose the experience you are looking for: Work or Personal. Type in your prompt. For example, “Write me a rap song about using M365. Adding a melody to the song: After you have generated and are happy with the lyrics, go to www.suno.com. Sign in if you have an existing account or create a free account by clicking on either "Create" or "Library" on the left-hand tab. 3. Once in Suno, toggle the "Custom" button and add your lyrics from Copilot in the "Write with Suno" box. Next, select your style of music, from pianist to female vocalist to country—they've got it all. Give your song a title and click “Create.” Suno will create two versions of your song in your song's library. 7. Example songs I created about M365: https://suno.com/song/853d0113-b36f-407a-b533-18b8ab27d681?sh=yn9LWYlho00RILtt https://suno.com/song/c4584819-ae68-4812-860b-897eb2d0367d?sh=Ewzn7SJ03VCYnuSm So, there you have it! With Copilot and Suno AI by your side, the world of music creation is at your fingertips. Whether you're crafting heartfelt lyrics or composing captivating melodies, these AI tools are here to elevate your creativity and make the process a breeze. So, grab your mic, fire up your laptop, and let Copilot and Suno AI help you create your next hit. Happy songwriting! For additional tips and resources, be sure to follow our Nonprofit Community | Microsoft Community Hub for blogs, updates, and expert advice on maximizing your nonprofit tools.Scaling AI: Strategies for Transforming Your Nonprofit
Scaling AI in a nonprofit context involves integrating AI technologies across various functions to maximize benefits such as improved decision-making, increased productivity, and enhanced service delivery. This can help nonprofits better serve their communities and achieve their missions more effectively. Key Strategies for Scaling AI in Nonprofits 1. Develop a Clear AI Strategy: Align the AI strategy with the nonprofit's mission and goals. Identify specific AI use cases that can drive value, such as automating administrative tasks, improving donor management, or enhancing service delivery. Create a roadmap for implementation, detailing resources, timelines, and milestones. Microsoft Tools that can help: Azure AI: Helps in developing and implementing AI strategies aligned with organizational goals. Power BI: Assists in identifying and visualizing AI use cases and creating implementation roadmaps. 2. Invest in AI Infrastructure: Utilize scalable cloud platforms like Azure to support data processing, storage, and analysis. Leverage tools like Azure Machine Learning to develop and deploy AI models efficiently. Microsoft Tools that can help: Azure: Provides scalable cloud infrastructure for data processing and storage. Azure Machine Learning: Facilitates the development and deployment of AI models. 3. Foster a Data-Driven Culture: Promote data literacy within the organization and encourage staff to use data in decision-making processes. Provide training and resources on AI and data analytics to build a data-driven culture. Microsoft Tools that can help: Microsoft Learn: Offers training resources on AI and data analytics. Power BI: Enhances data literacy by providing tools for data visualization and analysis. 4. Leverage Pre-Built AI Solutions: Use pre-built AI solutions and services to accelerate the scaling process. For example, Azure Cognitive Services offers APIs for vision, speech, language, and decision-making that can be integrated into nonprofit applications without building models from scratch. Microsoft Tool that can help: Azure AI Services: Provides pre-built AI solutions for vision, speech, language, and decision-making. 5. Implement Governance and Ethical Guidelines: Establish governance and ethical guidelines to ensure responsible and transparent use of AI. Address concerns related to bias, privacy, and accountability by following frameworks like Microsoft's Responsible AI principles. Microsoft Tool that can help: Microsoft Responsible AI: Offers frameworks and guidelines for ethical AI use. Real-World Examples of Scaling AI in Nonprofits: Disaster Response: AI can improve efficiency in disaster response by automating data collection and analysis, enabling faster and more accurate decision-making. Education: Nonprofits focused on education can use AI to provide personalized support to students, such as automating responses to common inquiries and offering tailored learning resources. Fundraising: AI can enhance fundraising efforts by analyzing donor data to identify potential major donors and optimize fundraising campaigns. Conclusion Scaling AI in a nonprofit organization requires a strategic approach, robust infrastructure, and a data-driven culture. By developing a clear AI strategy, investing in the right tools and technologies, and fostering an environment that embraces data and AI, nonprofits can unlock the full potential of AI and drive significant impact. Embrace the power of AI to take your nonprofit organization to new heights and better serve your community. For more detailed information and a comprehensive guide on scaling AI in your organization, you can explore the Scale AI in your organization module on Microsoft Learn.Empowering Nonprofits with Azure AI Vision: Enhancing Operations and Achieving Missions
Azure AI Vision is a powerful tool that enables organizations to analyze images and extract meaningful descriptions. Nonprofits can leverage AI Vision in various impactful ways to enhance their operations and achieve their missions more effectively. Here are some practical applications: Smart Stores Nonprofits operating retail stores can implement "smart store" solutions using AI Vision. By analyzing images from store cameras, they can identify customers needing assistance and direct employees to help them, improving customer service and operational efficiency. Event Management AI Vision can analyze images and videos from events to monitor attendance, track engagement, and gather insights on participant behavior. This data can help nonprofits improve future events and tailor their programs to better meet the needs of their audience. Security and Safety AI Vision can be used for security purposes, such as monitoring premises, detecting unauthorized access, and ensuring the safety of staff and beneficiaries. This technology can also identify potential hazards and prevent accidents. Now that we understand what Azure AI Vision is and some practical use cases, let's analyze a few images. Creating a Project in Azure AI Foundry Portal Pre-req: You will need to have an Azure account Navigate to Azure AI Foundry: Open a browser tab and go to the Azure AI Foundry portal. Sign In: Use your account credentials to sign in. 3. Create a Project: On the home page, select "Create a project." Projects in Azure AI Foundry help organize your work. 4. Configure Project and Hub: You will see a generated project name. Depending on your previous hub creations, you will either see new Azure resources to be created or a drop-down list of existing hubs. Select "Create new hub," name your hub, and proceed. 5. Customize Location: Select a location for your Azure AI services resource (East US, France Central, Korea Central, West Europe, or West US) and create the project. Resources Created Take note of the resources that are created: Azure AI services Azure AI hub Azure AI project Storage account Key vault Resource group After the resources are created, you will be brought to your project’s Overview page. Using AI Services Access AI Services: On your project's Overview page, select "AI Services" from the left-hand menu. Select Vision + Document: On the AI Services page, choose the Vision + Document tile to explore Azure AI Vision capabilities. Generating Captions for Images Image Captioning: On the Vision + Document page, select "Image" under "View all other vision capabilities," then choose the Image captioning tile. Upload Image: Upload an image by dragging it to the Drag and drop files here box, or by browsing to it on your file system. For demo purposes I used a provided sample file. Observe Captions: The Caption functionality generates a single, human-readable sentence describing the image's content. Dense Captioning Dense Captions: Return to the Vision + Document page, select the Image tab, and choose the Dense captioning tile. Upload Image: Upload an image by dragging it to the Drag and drop files here box, or by browsing to it on your file system. For demo purposes I used a provided sample file. Multiple Captions: Dense Captions provide multiple human-readable captions for an image, each describing essential objects detected in the picture. Tagging Images Extract Tags: On the Vision + Document page, select the Image tab and choose the Common tag extraction tile. Upload Image: Upload an image by dragging it to the Drag and drop files here box, or by browsing to it on your file system. For demo purposes I used a provided sample file. Review Tags: Extracted tags include objects and actions, with confidence scores indicating the likelihood of accurate descriptions. Conclusion Azure AI Vision is a transformative tool that nonprofits can leverage to enhance their operations and achieve their missions more effectively. By implementing smart store solutions, nonprofits can improve customer service and operational efficiency. Event management can be optimized by analyzing images and videos to monitor attendance and track engagement. Security and safety measures can be strengthened by using AI Vision to monitor premises and detect potential hazards. As we have explored the practical applications of Azure AI Vision, it's clear that this technology offers significant benefits for nonprofits. By integrating AI Vision into their operations, nonprofits can make data-driven decisions, improve their services, and ultimately better serve their communities. To learn more about Computer Vision and analyzing images, please visit: Fundamentals of Computer Vision - Training | Microsoft Learn.Empower Your Nonprofit with Azure AI: Building a Smart Knowledge Base
In today's digital age, providing quick and accurate answers to customer queries is essential for any business. Azure AI Language Studio offers powerful question answering capabilities that enable organizations to create and train a knowledge base of questions and answers. Creating a knowledge base with Azure AI Language Studio can be incredibly beneficial for a nonprofit organization. Here are some compelling reasons why you might want to consider it: Enhancing Customer Support: A knowledge base allows you to provide quick and accurate answers to common questions from your supporters, volunteers, and beneficiaries. This can significantly improve customer support by reducing response times and ensuring that inquiries are handled efficiently. Streamlining Operations: By automating the process of answering frequently asked questions, you can free up your staff to focus on more strategic tasks. This can lead to increased productivity and better use of resources, which is crucial for nonprofits operating with limited budgets. Improving Accessibility: A well-structured knowledge base can make information more accessible to everyone, including individuals with disabilities. Azure AI Language Studio's natural language processing capabilities ensure that users can find the information they need easily, regardless of how they phrase their questions. Enhancing Engagement: Providing timely and accurate information can enhance engagement with your supporters and beneficiaries. When people feel that their questions are answered promptly, they are more likely to stay involved and support your cause. Data-Driven Insights: Azure AI Language Studio can provide valuable insights into the types of questions being asked and the information that is most sought after. This data can help you understand the needs and concerns of your community better and tailor your programs and services accordingly. Cost-Effective Solution: Creating a knowledge base with Azure AI Language Studio is a cost-effective solution for managing inquiries. It reduces the need for extensive human intervention and can be scaled easily as your organization grows. Now that we understand the importance, let's build our first knowledge base! Creating a Language Resource To get started, you need to create a Language resource in the Azure portal: Open the Azure portal at https://portal.azure.com/?azure-portal=true and sign in with your Microsoft account. Click the "Create a resource." Search for "Language service." Select "Create a language service plan" and configure the following settings: Default features: Keep the default features. Custom features: Select custom question answering. On the Create Language page, specify the following settings: Subscription: Your Azure subscription. Resource group: Select an existing resource group or create a new one. Region: Select a region (e.g., East US 2). Name: A unique name for your language resource. Pricing tier: Select a pricing tier. Azure search region: Any available location. Azure search pricing tier: Select your Azure search pricing tier. 6. Review and create the resource and wait for the deployment to complete. Creating a New Project Next, you'll create a new project in Language Studio: Open the Language Studio portal at Language Studio - Microsoft Azure and sign in with your Microsoft account. If prompted, select your Azure directory, subscription, and Language resource. In the Create new menu, select "Custom question answering." On the Choose language setting for resource page, select "I want to select the language when I create a project in this resource" and click Next. Enter the following details on the Enter basic information page: Language resource: Choose your Language resource. Azure search resource: Choose your Azure search resource. Name: Choose a name Description: A simple knowledge base Source language: English Default answer when no answer is returned: No answer found Review and create the project. Adding Content to the Knowledge Base Now, you'll add content to your knowledge base from your appropriate document. From demo purposes I will be added the Q&A from our public NTA Landing Page. On the Manage sources page, select "Add source" and choose "URLs." In the Add URLs box, enter the following details: URL name: Enter URL name, for demo purposes I used NTA. URL: Enter URL, for demo purposes I use the NTA Landing Page Classify file structure: Auto-detect Select "Add all" to add the URL to your knowledge base. Editing the Knowledge Base You can customize your knowledge base by adding custom question-and-answer pairs: Expand the left panel and select "Edit knowledge base." Select "+" to add a new question pair. In the Add a new question answer pair dialog box, enter your question as the answer, then select "Done." Save your knowledge base. Training and Testing the Knowledge Base Once your knowledge base is set up, you can train and test it: Select "Test" at the top of the Question answer pairs pane. In the test pane, enter one of your questions to see the response. Try other questions as well. Deploying the Knowledge Base Finally, you can deploy your knowledge base as a client application: In the left panel, select "Deploy knowledge base." Select "Deploy" at the top of the page and confirm the deployment. 3. Next step will be creating your bot. https://aka.ms/qna-create-bot By following these steps, you can create a robust knowledge base using Azure AI Language Studio, providing quick and accurate answers to your customers' questions. This not only enhances customer satisfaction but also streamlines your support processes. To learn more about language services, please visit: Fundamentals of question answering with the Language Service - Training | Microsoft LearnHarnessing the Power of Speech AI with Azure AI Foundry
In the ever-evolving world of artificial intelligence, speech technology stands out as a transformative tool that bridges the gap between humans and machines. Microsoft’s Azure AI Foundry portal offers a comprehensive suite of AI services, including powerful speech capabilities. Let's dive into how you can explore and leverage these features to enhance your projects. What is Azure AI Foundry? Azure AI Foundry is a platform designed to help developers and businesses create intelligent applications using a variety of AI services. It provides a centralized hub where you can access, manage, and deploy AI models, including those for speech recognition and synthesis. Key Features of Azure AI Speech Speech-to-Text: Azure AI Speech can transcribe spoken language into written text with high accuracy. This feature is invaluable for applications like meeting transcription, voice commands, and automated customer service. Text-to-Speech: Convert written text into natural-sounding speech. This capability is perfect for creating voice-enabled applications, audiobooks, and accessibility tools. Real-Time Transcription: The Speech Playground in Azure AI Foundry allows you to test live transcription capabilities on your own audio files without writing any code. This feature is ideal for quickly evaluating the performance of speech-to-text models. Expressive Voices: Browse a variety of humanlike voices to find the perfect match for your project. These voices can add a personal touch to your applications, making interactions more engaging and relatable. How to Get Started with Azure AI Speech Navigate to Azure AI Foundry: Open a browser tab and go to the Azure AI Foundry portal. Sign In: Use your account credentials to sign in. Create a Project: On the home page, select "Create a project." Projects in Azure AI Foundry help organize your work. Configure Project and Hub: You will see a generated project name. Depending on your previous hub creations, you will either see new Azure resources to be created or a drop-down list of existing hubs. Select "Create new hub," name your hub, and proceed. Resources Created Take note of the resources that are created: Azure AI services Azure AI hub Azure AI project Storage account Key vault Resource group After the resources are created, you will be brought to your project’s Overview page. Using AI Services Access AI Services: On your project's Overview page, select "AI Services" from the left-hand menu. 2. Select Speech: On the AI Services page, choose the speech tile to explore the Speech capabilities. 3. Test out the real-time transcription feature: Upload your audio files and see how Azure AI Speech transcribes them into text. This hands-on experience helps you understand the capabilities and performance of the service. 4. Transcription: Upload or record your audio files to see how Azure AI Speech transcribes them into text. 5. Deploy and Integrate: Once you’re satisfied with the performance, deploy your speech models to production. Integrate them into your applications to start leveraging the power of Azure AI Speech in real-world scenarios. Benefits of Using Azure AI Speech High Accuracy: Azure AI Speech offers state-of-the-art accuracy in speech recognition and synthesis, ensuring reliable performance for your applications. Scalability: Easily scale your speech applications to handle varying workloads, from small projects to enterprise-level deployments. Customization: Fine-tune models to meet the specific needs of your business, improving the relevance and effectiveness of speech interactions. Ease of Use: The intuitive interface and comprehensive documentation make it easy to get started, even for those new to AI and speech technologies. Conclusion Exploring speech capabilities in the Azure AI Foundry portal opens up a world of possibilities for creating intelligent, voice-enabled applications. Whether you’re looking to transcribe meetings, develop voice assistants, or enhance accessibility, Azure AI Speech provides the tools you need to succeed. Start your journey today and see how speech technology can transform your projects. To learn more about Azure AI Speech, please visit: Fundamentals of Azure AI Speech - Training | Microsoft Learn.Have No Fear, Responsible AI is Here: Empowering Organizations Dedicated to Social Good
Artificial Intelligence (AI) has the potential to transform industries in powerful ways, from optimizing operations to making smarter, data-driven decisions. But as organizations, including nonprofits, begin to integrate AI into their processes, ethical considerations must remain at the forefront. After all, the use of AI directly impacts individuals and communities and must align with the values of fairness, transparency, and accountability. Fortunately, Microsoft is at the forefront of advocating for responsible AI. The company is not only developing AI solutions but also ensuring these tools are ethical, inclusive, and beneficial to all—particularly nonprofits that are using AI to drive social good. In this blog, we’ll explore Microsoft’s approach to responsible AI and how organizations, particularly those dedicated to social good, can leverage its technologies to further their mission in a responsible and ethical manner. What is Responsible AI? Responsible AI refers to the practice of designing, developing, and deploying AI systems that are ethical, transparent, and accountable. Microsoft defines responsible AI as: Fairness: AI models should be unbiased and provide equitable outcomes for all. Reliability & Safety: AI must operate in a manner that is safe and predictable. Privacy & Security: AI systems should respect privacy and safeguard sensitive information. Inclusiveness: AI should be designed to serve diverse populations and include perspectives from different communities. Transparency: Nonprofits and stakeholders should understand how AI models make decisions. Accountability: Organizations should be responsible for the outcomes and impacts of their AI systems. Microsoft’s commitment to responsible AI ensures that the tools used in organizations can drive innovation and efficiency without compromising ethical standards. For more information on Microsoft’s approach to Responsible AI, please visit: Empowering responsible AI practices | Microsoft AI 1. Ethical AI for Social Good Microsoft recognizes that nonprofits play a crucial role in addressing global challenges, and they are eager to empower these organizations with AI to drive positive change. To help ensure that AI is deployed responsibly, Microsoft offers several key principles and resources: AI for Good Program Microsoft’s AI for Good program aims to harness the power of AI to help address some of the world’s most pressing issues, including climate change, healthcare, and education. The program is guided by Microsoft’s responsible AI principles, ensuring that AI is used in a way that maximizes social benefit while mitigating risks. Through the AI for Good Lab, Microsoft has partnered with various organizations to design and implement AI solutions that benefit marginalized communities. Examples include AI-powered solutions to predict disease outbreaks, monitor deforestation, or support accessibility for people with disabilities. To learn more about the initiatives around the AI for Good Program, please visit: AI For Good Lab - Microsoft Research AI for Accessibility The AI for Accessibility initiative seeks to empower groups with AI tools to create innovative solutions that improve the lives of people with disabilities. By promoting inclusivity in technology, organizations can develop applications that improve communication, mobility, and daily living for individuals with visual, auditory, or cognitive impairments. To learn more about Microsoft’s Accessibility Innovation, please visit: Innovation and AI for Accessibility | Microsoft Accessibility 2. Microsoft’s Responsible AI Tools and Frameworks Microsoft doesn’t just talk the talk; they provide tools and frameworks that help organizations ensure their AI is being developed and deployed responsibly. These tools can be extremely helpful for nonprofits seeking to maintain ethical AI practices: The Microsoft Responsible AI Standard The Responsible AI Standard is a set of guidelines designed to ensure that AI systems are ethical, fair, and inclusive. It can provide nonprofits with a clear path for identifying and mitigating biases, ensuring AI models are transparent, and keeping privacy concerns at the forefront. This framework includes specific actions, including: Fairness assessments to identify biases in AI models. Data privacy protocols to safeguard sensitive information. Transparency guidelines that allow users to understand how AI decisions are made. By integrating the Responsible AI Standard, organizations, especially nonprofits can confidently adopt AI tools while ensuring their use aligns with ethical standards. To learn more about Microsoft’s principles and approach to responsible AI, please visit: Responsible AI Principles and Approach | Microsoft AI These are just a few of the resources put in place to ensure that AI is developed and used responsibly, benefiting society as a whole. By adhering to these principles and utilizing the tools provided by Microsoft, organizations, especially nonprofits, can harness the power of AI to drive positive change while upholding ethical standards. As AI continues to evolve, it is crucial to prioritize responsible practices to build a future where technology serves the greater good and fosters trust within communities. For additional tips and resources, be sure to follow our Nonprofit Community | Microsoft Community Hub for blogs, updates, and expert advice on maximizing your nonprofit tools.
