Demystifying Azure AI Foundry: A Beginner's Guide
Are you ready to dive into the transformative world of Artificial Intelligence but unsure where to start? You landed at the right page. Welcome! Whether you’re just getting started or have a bit of experience under your belt, this read will provide you with the foundational knowledge needed to unlock the potential of Azure AI Foundry. Azure AI Foundry offers a powerful suite of tools and Machine Learning Models. Let’s start harnessing its capabilities as an initial step to create innovative AI solutions that can revolutionize your projects or business. As a Microsoft Technical Trainer, delivering classes on AI and Data for the past several years, I have witnessed the significant impact AI has had across various industries. My objective here is to equip you with the foundational knowledge and confidence necessary to embark on your AI journey. Azure AI Foundry (formerly Azure AI Studio) is a suite of tools that makes artificial intelligence accessible to everyone. It allows users to build, deploy, and manage AI solutions easily. Azure AI Foundry can be leveraged to address real-world business challenges and foster innovation. For instance, Azure AI Foundry can enhance predictive maintenance in manufacturing by analyzing sensor data to foresee equipment failures to minimize downtime and costs. In retail, it can perform customer sentiment analysis across social media, reviews, and surveys, offering insights into customer satisfaction and areas for improvement. For more ways that Azure AI Foundry can help your organization, see customer stories here: Azure AI Foundry - Generative AI Development Hub and here: Need inspirations? Real AI Apps stories by Azure customers to help you get started With Azure AI Foundry, you can explore and develop various AI models and services tailored to your goals. The platform supports scalability, enabling proof of concepts to become full production applications effortlessly. It also has support for continuous monitoring and refinement ensuring long-term success. Here's a brief overview of AI Foundry's main architectural components and their integration. Azure AI Foundry architecture - Azure AI Foundry At the top level, AI Foundry provides access to the following resources: Management Center: Used to manage AI Foundry resources like hubs, projects, connected resources, and deployments. It is a part of the Azure AI Foundry portal that streamlines governance and management activities. In the management center, you can view and manage: Projects and resources Quotas and usage metrics Govern access and permissions For more information see: Management center overview - Azure AI Foundry AI Foundry Hub: The main top-level resource in the AI Foundry portal, offering a centralized method for managing security, connectivity, and computing resources across playgrounds and projects. Once a hub is established, developers can create projects from it and access shared resources like storage accounts, key vaults, databases and others without requiring continuous assistance from an IT administrator. The hub uses the Azure Machine Learning service and this Azure Provider, Microsoft.MachineLearningServices/workspaces and the type is hub. It offers: Security features, including a managed network for projects and model endpoints. Computer resources for development, fine-tuning, open-source, and serverless model deployments. Connections to other Azure services, such as Azure OpenAI and Azure AI Search. An Azure storage account for data and artifacts. AI Foundry Project: A project is part of the hub. Projects help organize work, save state across tools like prompt flow and enable collaboration. You can share files and data source connections within a project. Hubs support multiple projects and users. Projects manage billing, access, and provide data isolation, using dedicated storage containers to securely share files among project members. Once you have a project, you can connect to it from your code. You can explore models and capabilities before creating a project, but once you're ready to build, customize, test, and operationalize, a project is where you'll want to be. The Azure provider for a project is Microsoft.MachineLearningServices/workspaces, and the type is Project. The project offers: Reusable assets like datasets, models, and indexes. A container for uploading data within the hub's storage. Private data access for project members. Model deployments from the catalog and adjusted model endpoints. Connections: AI Foundry hubs and projects use connections to access resources from other services, such as an Azure Storage Account, Azure OpenAI, or other Azure AI services. Once you have setup the Azure Foundry Hub, Project and Connection, its time to start exploring and deploying the models. Model Catalog: Available in Azure AI Foundry portal to discover and use a wide range of models for building generative AI applications. The model catalog features hundreds of models across model providers such as Azure OpenAI Service, Meta, NVIDIA, Hugging Face, DeepSeek, and of course the models trained by Microsoft like Phi. Azure AI Model Catalog – Foundation Models You can search and discover models that meet your needs. The model catalog also offers the model performance benchmark metrics which can be accessed by using Compare Models feature or from the model card Benchmark tab. Models need to be deployed to make them available for receiving inference requests. Azure AI Foundry offers a comprehensive suite of deployment options for those models depending on your needs and model requirements. Prompt Flow: This feature can be used to generate, customize, or run a flow. A flow is an executable instruction set that can implement the AI logic. Flows can be created or run via multiple tools, like a prebuilt canvas, LangChain, etc. Iterations of a flow can be saved as assets; once deployed a flow becomes an API. Prompt flow in Azure AI Foundry portal - Azure AI Foundry A prompt is sent to a model, consisting of the user input, system message, and any examples. User input is text submitted in the chat window. System message is a set of instructions to the model scoping its behaviors and functionality. Evaluators: Helpful tools to assess the frequency and severity of content risks or undesirable behavior in AI responses. Performing iterative, systematic evaluations with the right evaluators can help teams measure and address potential response quality, safety, or security concerns throughout the AI development lifecycle. You can explore Azure AI Foundry benchmarks to evaluate and compare models on publicly available datasets. Deploy: Remember to deploy your model setup. Deployments are hosted within an endpoint and can receive data from clients and send responses back in real-time. You can invoke the endpoint for real-time inference for chat, copilot or another generative AI application. AI Foundry has all the needed capabilities including content filters, Responsible AI factors and Security. More about these aspects in my next article. Playgrounds: Be sure to test your model using the Playgrounds on Azure AI Foundry. The portal offers a chat playground for deploying and interacting with AI chat models, allowing you to refine them before production deployment. Hear and speak with chat models in the Azure AI Foundry portal chat playground - Azure AI Foundry Now that you have the foundational knowledge, I encourage you to begin using Azure AI Foundry by following the resources and guidelines provided. Ready to get started with Azure AI Foundry? Here are some tutorials to guide you: Use the chat playground in Azure AI Foundry portal Tutorial: Deploy an enterprise chat web app in the Azure AI Foundry portal playground Explore, learn, and transform your ideas into reality with Azure AI Foundry. Stay tuned for more! Happy Learning!Azure Course Blueprints
Please refer to the updated document now in Azure Architecture Blog https://aka.ms/courseblueprint Overview The Course Blueprint is a comprehensive visual guide to the Azure ecosystem, integrating all the resources, tools, structures, and connections covered in the course into one inclusive diagram. It enables students to map out and understand the elements they've studied, providing a clear picture of their place within the larger Azure ecosystem. It serves as a 1:1 representation of all the topics officially covered in the instructor-led training. Links: Each icon in the blueprint has a hyperlink to the pertinent document in the learning path on Learn. Layers: You have the capability to filter layers to concentrate on segments of the course by modules. I.E.: Just day 1 of AZ-104, using filters in Visio and selecting modules 1-3 Enhanced Integration: The Visio Template+ for expert courses such as SC-100 and AZ-305 now features an additional layer that allows you to compare SC-100, AZ-500, and SC-300 within the same diagram. Similarly, you can compare any combination of AZ-305, AZ-204, AZ-700, and AZ-104 to identify differences and study gaps. Since SC-300 and AZ-500 are potential prerequisites for SC-100, and AZ-204 or AZ-104 for AZ-305, this comparison is particularly useful for understanding the extra knowledge or skills required to advance to the next level. Advantages for Students Defined Goals: The blueprint presents learners with a clear vision of what they are expected to master and achieve by the course’s end. Focused Learning: By spotlighting the course content and learning targets, it steers learners’ efforts towards essential areas, leading to more productive learning. Progress Tracking: The blueprint allows learners to track their advancement and assess their command of the course material. New Feature: A comprehensive list of topics for each slide deck is now available in a downloadable .xlsx file. Each entry includes a link to Learn and its dependencies. Download links Associate Level PDF Visio Released Updated Contents! AZ-104 Azure Administrator Associate Blueprint [PDF] Template 12/14/2023 10/28/2024 Contents AZ-204 Azure Developer Associate Blueprint [PDF] Template 11/05/2024 11/11/2024 Contents AZ-500 Azure Security Engineer Associate Blueprint [PDF] Template+ 01/09/2024 10/10/2024 Contents AZ-700 Azure Network Engineer Associate Blueprint [PDF] Template 01/25/2024 11/04/2024 Contents SC-300 Identity and Access Administrator Associate Blueprint [PDF] Template 10/10/2024 Contents Specialty PDF Visio Released Updated AZ-140 Azure Virtual Desktop Specialty Blueprint [PDF] Template 01/03/2024 02/05/2024 Expert level PDF Visio Released Updated AZ-305 Designing Microsoft Azure Infrastructure Solutions Blueprint [PDF] Template+ AZ-104 AZ-204 AZ-700 05/07/2024 12/09/2024 Contents SC-100 Microsoft Cybersecurity Architect Blueprint [PDF] Template+ AZ-500 SC-300 10/10/2024 Contents Skill based Credentialing PDF Visio Released Updated AZ-1002 Configure secure access to your workloads using Azure virtual networking Blueprint [PDF] Template 05/27/2024 Contents AZ-1003 Secure storage for Azure Files and Azure Blob Storage Blueprint [PDF] Template 02/07/2024 02/05/2024 Contents Benefits for Trainers: Trainers can follow this plan to design a tailored diagram for their course, filled with notes. They can construct this comprehensive diagram during class on a whiteboard and continuously add to it in each session. This evolving visual aid can be shared with students to enhance their grasp of the subject matter. Introduction to Course Blueprint for Trainers [10 minutes + comments] Real life demo AZ-104 Advanced Networking section [3 minutes] Visio stencils Azure icons - Azure Architecture Center | Microsoft Learn Subscribe if you want to get notified of any update like new releases or updates. My email ilan.nyska@microsoft.com LinkedIn https://www.linkedin.com/in/ilan-nyska/ Celebrating 30,000 Downloads! Please consider sharing your anonymous feedback <-- [~ 40 seconds to complete]🚀 Mastering Microsoft Power Platform Fundamentals – My Learning Journey & Tips!
I recently completed the Power Platform Fundamentals Learning Plan and wanted to share my journey, key takeaways, and useful tips for anyone looking to get certified! 💡 What I learned: ✔ How Power Automate simplifies workflows ✔ Dataverse best practices ✔ Custom app-building insights Here’s the official Microsoft Learn Plan I followed: [Power Platform Fundamentals Learning Plan] 📢 If you're preparing for the exam or just exploring Power Platform, let’s connect and discuss! Feel free to ask any questions. 🚀Announcing GitHub Universe Cloud Skills Challenge!
Join the GitHub Universe Cloud Skills Challenge and start your exiting journey in AI! Whether you’re beginning or looking to change your career, this learning experience is designed to introduce you to some of the most requested GitHub tools for AI beginners, and to explore new opportunities. Join the GitHub Universe Cloud Skills Challenge and start your exciting journey in AI!Embracing Responsible AI: Measure and Mitigate Risks for a Generative AI App in Azure AI Studio
Artificial intelligence has taken the world by storm, redefining the way businesses operate and innovate. Whether you're an experienced developer or a beginner looking to break into the world of AI, Azure AI Studio offers a robust platform for creating cutting-edge AI applications responsibly and securely. I recently had the opportunity to dive into the Microsoft Learn module: Measure and Mitigate Risks for a Generative AI App in Azure AI Studio. It’s an incredible resource that walks you through every step of building and refining a responsible AI application. Today, I’d like to share my experience and encourage you to embark on this journey too, gaining essential skills in the process.
