Get certified as an Azure AI Engineer (AI-102) this summer?
For developers, the accreditation as an Azure AI Engineer—certified through the rigorous AI-102 exam—has become a golden ticket to career acceleration. It isn’t just about coding chatbots or fine-tuning machine learning models; it’s about gaining the confidence (for you and for your business) that you can wield Azure’s toolkits to configure AI solutions that augment human capability. Before we dive in, if you’re planning to become certified as an Azure AI Engineer, you may find this Starter Learning Plan (AI 102) valuable—recently curated by a group of Microsoft experts, purposed for your success. We recommend adding it to your existing learning portfolio. It’s a light introduction that should take less than four hours, but it offers a solid glimpse into what to expect on your journey and the breadth of solutions you might craft in the future. From revolutionizing customer service with intelligent agents to optimizing supply chains through predictive analytics, Azure AI engineers sit at the confluence of technological ingenuity and business transformation. For those with an appetite for problem-solving and a vision for AI-driven futures, this certification isn’t just another badge—it’s an assertion of expertise in a field where demand is outpacing supply. Securing that expertise, however, requires more than just a weekend of cramming. Today’s aspiring AI engineers navigate an ecosystem of learning that is as modern as the field itself. Gone are the days when one could rely solely on a stack of manuals; now, candidates immerse themselves in a medley of Microsoft Learn modules, hands-on labs, AI-powered coding assistants, and community-led study groups. Many take a pragmatic approach—building real-world projects using Azure Cognitive Services and Machine Learning Studio to cement their understanding. Others lean on practice exams and structured courses from platforms like Pluralsight and Udemy, ensuring they aren’t just memorizing but internalizing the core principles. The AI-102 exam doesn’t reward rote knowledge—it demands fluency in designing, deploying, and securing AI solutions, making thorough preparation an indispensable part of the journey. In addition to the above learning plan, we want to provide a few other tips. Understand the Exam Objectives: Begin by thoroughly reviewing the AI-102 study guide. This document outlines the key topics and skills assessed, including planning and managing Azure AI solutions, implementing computer vision and natural language processing solutions, and deploying generative AI solutions. Familiarizing yourself with these areas will provide a structured framework for your study plan. Continuous memorization is part of your study. But if you get a bit bored from your flashcards and look for more ‘storyline’ style learning content, we recommend adding MSFT employee created learning plan to your mix. They are scenario-based and focus more on providing you with a structured understanding of how to do XYZ on Azure. Here are 3 examples: Modernize for AI Readiness Build AI apps with Azure Re-platform AI applications Hands-On Practice: Practical experience is invaluable. Engage with Azure AI services directly by building projects that incorporate computer vision, natural language processing, and other AI functionalities. This hands-on approach not only reinforces theoretical knowledge but also enhances problem-solving skills in real-world scenarios. Utilize Practice Assessments: Assess your readiness by taking advantage of free practice assessments provided by Microsoft. These assessments mirror the style and difficulty of actual exam questions, offering detailed feedback and links to additional resources for areas that may require further study. Stay Updated on Exam Changes: Certification exams are periodically updated to reflect the latest technologies and practices. Regularly consult the official exam page to stay informed about any changes in exam content or structure. Participate in Community Discussions: Engaging with peers through forums and study groups can provide diverse perspectives and insights. The Microsoft Q&A platform is a valuable resource for asking questions, sharing knowledge, and learning from the experiences of others preparing for the same certification. By systematically incorporating these strategies into your preparation, you'll be well-positioned to excel in the AI-102 exam and advance your career as an Azure AI Engineer. If you have additional tips or thoughts, let us know in the comments area. Good luck!
Learn how to develop innovative AI solutions with updated Azure skilling paths
The rapid evolution of generative AI is reshaping how organizations operate, innovate, and deliver value. Professionals who develop expertise in generative AI development, prompt engineering, and AI lifecycle management are increasingly valuable to organizations looking to harness these powerful capabilities while ensuring responsible and effective implementation. In this blog, we’re excited to share our newly refreshed series of Plans on Microsoft Learn that aim to supply your team with the tools and knowledge to leverage the latest AI technologies, including: Find the best model for your generative AI solution with Azure AI Foundry Create agentic AI solutions by using Azure AI Foundry Build secure and responsible AI solutions and manage generative AI lifecycles From sophisticated AI agents that can autonomously perform complex tasks to advanced chat models that enable natural human-AI collaboration, these technologies are becoming essential business tools rather than optional enhancements. Let’s take a look at the latest developments and unlock their full potential with our curated training resources from Microsoft Learn. Simplify the process of choosing an AI model with Azure AI Foundry Choosing the optimal generative AI model is essential for any solution, requiring careful evaluation of task complexity, data requirements, and computational constraints. Azure AI Foundry streamlines this decision-making process by offering diverse pre-trained models, fine-tuning capabilities, and comprehensive MLOps tools that enable businesses to test, optimize, and scale their AI applications while maintaining enterprise-grade security and compliance. Our Plan on Microsoft Learn titled Find the best model for your generative AI solution with Azure AI Foundry will guide you through the process of discovering and deploying the best models for creating generative AI solutions with Azure AI Foundry, including: Learn about the differences and strengths of various language models Find out how to integrate and use AI models in your applications to enhance functionality and user experience. Rapidly create intelligent, market-ready multimodal applications with Azure models, and explore industry-specific models. In addition, you’ll have the chance to take part in a Microsoft Azure Virtual Training Day, with interactive sessions and expert guidance to help you skill up on Azure AI features and capabilities. By engaging with this Plan on Microsoft Learn, you’ll also have the chance to prove your skills and earn a Microsoft Certification. Leap into the future of agentic AI solutions with Azure After choosing the right model for your generative AI purposes, our next Plan on Microsoft Learn goes a step further by introducing agentic AI solutions. A significant evolution in generative AI, agentic AI solutions enable autonomous decision-making, problem-solving, and task execution without constant human intervention. These AI agents can perceive their environment, adapt to new inputs, and take proactive actions, making them valuable across various industries. In the Create agentic AI solutions by using Azure AI Foundry Plan on Microsoft Learn, you’ll find out how developing agentic AI solutions requires a platform that provides scalability, adaptability, and security. With pre-built AI models, MLOps tools, and deep integrations with Azure services, Azure AI Foundry simplifies the development of custom AI agents that can interact with data, make real-time decisions, and continuously learn from new information. You’ll also: Learn how to describe the core features and capabilities of Azure AI Foundry, provision and manage Azure AI resources, create and manage AI projects, and determine when to use Azure AI Foundry. Discover how to customize with RAG in Azure AI Foundry, Azure AI Foundry SDK, or Azure OpenAI Service to look for answers in documents. Learn how to use Azure AI Agent Service, a comprehensive suite of feature-rich, managed capabilities, to bring together the models, data, tools, and services your enterprise needs to automate business processes There’s also a Microsoft Virtual Training Day featuring interactive sessions and expert guidance, and you can validate your skills by earning a Microsoft Certification. Safeguard your AI systems for security and fairness Widespread AI adoption demands rigorous security, fairness, and transparency safeguards to prevent bias, privacy breaches, and vulnerabilities that lead to unethical outcomes or non-compliance. Organizations must implement responsible AI through robust data governance, explainability, bias mitigation, and user safety protocols, while protecting sensitive data and ensuring outputs align with ethical standards. Our third Plan on Microsoft Learn, Build secure and responsible AI solutions and manage generative AI lifecycles, is designed to introduce the basics of AI security and responsible AI to help increase the security posture of AI environments. You’ll not only learn how to evaluate and improve generative AI outputs for quality and safety, but you’ll also: Gain an understanding of the basic concepts of AI security and responsible AI to help increase the security posture of AI environments. Learn how to assess and improve generative AI outputs for quality and safety. Discover how to help reduce risks by using Azure AI Content Safety to detect, moderate, and manage harmful content. Learn more by taking part in an interactive, expert-guided Microsoft Virtual Training Day to deepen your understanding of core AI concepts. Got a skilling question? Our new Ask Learn AI assistant is here to help Beyond our comprehensive Plans on Microsoft Learn, we’re also excited to introduce Ask Learn, our newest skilling innovation! Ask Learn is an AI assistant that can answer questions, clarify concepts, and define terms throughout your training experience. Ask Learn is your Copilot for getting skilled in AI, helping to answer your questions within the Microsoft Learn interface, so you don’t have to search elsewhere for the information. Simply click the Ask Learn icon at the top corner of the page to activate! Begin your generative AI skilling journey with curated Azure skilling Plans Azure AI Foundry provides the necessary platform to train, test, and deploy AI solutions at scale, and with the expert-curated skilling resources available in our newly refreshed Plans on Microsoft learn, your teams can accelerate the creation of intelligent, self-improving AI agents tailored to your business needs. Get started today! Find the best model for your generative AI solution with Azure AI Foundry Create agentic AI solutions by using Azure AI Foundry Build secure and responsible AI solutions and manage generative AI lifecyclesRAG Time: Ultimate Guide to Mastering RAG!
RAG Time is a brand-new AI learning series designed to help developers unlock the full potential of Retrieval-Augmented Generation (RAG). If you’ve been looking for a way to build smarter, more efficient AI systems—join us in RAG Time, every Wednesday 9AM PT from March 5 through April 2 on Microsoft Developer YouTube. What's in RAG Time? RAG Time is a five-part learning journey, with new videos and blog posts releasing every week in March. The series features: 🔥 Expert-led discussions breaking down RAG fundamentals and best practices 🎤 Exclusive leadership interviews with AI leaders ⚡ Hands-on demos & real-world case studies showing RAG in action 🎨 Creative doodle summaries making complex concepts easier to grasp and remember 🛠 Samples & resources in the RAG Time repository so you can start building today What You’ll Learn The series is structured into five learning journeys, each tackling a crucial aspect of RAG-powered AI: 📌 March 5th, 9AM PT - Journey 1: RAG and Knowledge Retrieval Fundamentals – Start with the basics! Learn how RAG, search indexing, and vector search work together to create smarter AI retrieval systems. 📌 March 12th, 9AM PT - Journey 2: Build the Ultimate Retrieval System for RAG – Go beyond the fundamentals with hybrid search, semantic ranking, and relevance tuning to refine how AI retrieves the most relevant information. 📌 March 19th, 9AM PT - Journey 3: Optimize Your Vector Index for Scale – Learn how to scale vector search efficiently, optimize storage, and implement advanced techniques like quantization and Matryoshka learning for large-scale AI applications. 📌 March 26th, 9AM PT - Journey 4: RAG for All Your Data: Multimodal and Beyond – Move beyond text-based retrieval! Discover how to integrate images, audio, and structured data into your RAG workflows and leverage multimodal pipelines for next-level AI capabilities. 📌 April 2nd, 9AM PT - Journey 5: Hero Use Cases for RAG – Explore real-world implementations, industry-leading examples, and best practices, while diving into Responsible AI considerations to ensure ethical and impactful solutions. Why You Should Watch If you're a developer, data scientist, or AI enthusiast, this series is built for you! Whether you’re just getting started or looking to master enterprise-grade retrieval systems, RAG Time delivers practical knowledge, hands-on resources, and expert insights to help you stay ahead. Journey starts here 🚀 Start your journey from the RAG Time repo: https://aka.ms/rag-time. You'll find all the information about the video series, samples, documentation and doodles in the repo! Share your experience and feedback on GitHub discussions.Strengthen your responsible AI stance with Microsoft Learn Plans
As AI's transformative power pervades industries, the call for responsible and trustworthy AI frameworks has never been more urgent. Businesses worldwide are recognizing the importance of these frameworks in maintaining regulatory compliance, enhancing brand reputation, and building public trust. Microsoft has been putting our Responsible AI principles into action since 2017 with evolving tools and best practices to ensure this technology is used in a way that is driven by ethical principles that put people first. In this blog, we explore the concepts of Trustworthy and Responsible AI and how companies can leverage these frameworks to drive value and reduce risk. We’ll also introduce a pair of official Plans on Microsoft Learn to help your team start building your own Responsible AI practices, as well as a new episode of our Azure Enablement Show that provides further insights into Microsoft's commitment to Responsible AI and a practical demo of new AI content safety features. Ethical AI: A growing market opportunity Analysts project substantial growth in Responsible and Trustworthy AI markets, driven by companies’ need to integrate ethical and reliable AI to stay competitive. The increasing AI regulation and compliance requirements globally, such as the EU AI Act, are pushing companies to adopt Responsible and Trustworthy AI frameworks to ensure compliance and avoid penalties. Additionally, rising consumer and public expectations for ethical AI applications impact brand reputation and consumer trust. Companies that prioritize ethical AI practices are more likely to gain consumer trust and loyalty. Avoiding Responsible AI roadblocks Adopting Responsible and Trustworthy AI frameworks isn’t without obstacles, especially when addressing the six foundational pillars laid out by Microsoft: Fairness: Mitigating biases in data and algorithms to ensure AI systems treat all individuals equitably and make unbiased, non-discriminatory decisions across diverse populations. Reliability and Safety: Ensuring AI performs consistently under diverse scenarios through rigorous testing, minimizing errors, and avoiding unintended consequences in critical applications. Privacy and Security: Protecting sensitive data by complying with regulations like GDPR and mitigating potential cybersecurity threats targeting AI systems and processes. Inclusiveness: Designing AI systems that are accessible and equitable, engaging diverse user needs, including underserved or marginalized communities. Transparency: Making AI decision-making processes understandable by explaining how outputs are generated, overcoming the “black box” nature of many models. Accountability: Establishing clear responsibility for AI decisions and actions across developers, operators, and stakeholders to ensure ethical use. Overcoming these challenges ensures compliance, builds public trust, and safeguards brand reputation in an AI-driven future. Companies that prioritize ethical and secure AI have a competitive edge in attracting customers who value responsible innovation. Trustworthy AI vs. Responsible AI: Understanding the difference To navigate the landscape of ethical AI, it’s essential to distinguish between Trustworthy AI and Responsible AI. Though interconnected, each necessary framework has unique characteristics: Trustworthy AI emphasizes the reliability, robustness, and security of AI systems. Its core focus is on creating AI applications that users, businesses, and regulators can trust due to their predictability and protection against unauthorized access. In our official Plan on Microsoft Learn, Evolve with gen AI: Operationalize your Azure gen AI solutions with fine-tuning and prompt flow, we learn the fundamentals of AI security to help users understand the types of controls that apply to AI systems and the testing procedures that increase the security postures of AI environments. Responsible AI focuses on aligning AI systems with ethical standards and societal values, ensuring these technologies positively impact individuals and communities. The Plan on Microsoft Learn, Evolve with gen AI: Operationalize your Azure gen AI solutions with fine-tuning and prompt flow, also includes an overview of Responsible AI principles and practices designed to help you adopt responsible AI practices. It offers an overview of the principles, governance system, and procedures followed at Microsoft, but we encourage you to develop your own AI strategy. Responsible and Trustworthy AI from a platform perspective Together, the Trustworthy AI and Responsible AI frameworks ensure AI technology is safe, ethical, and beneficial for society. These frameworks incorporate measures like automated content moderation and prompt shields to protect users from harmful outputs while promoting accurate and contextually relevant responses. For instance, content safety features help detect toxic language or misinformation, while groundedness detection ensures AI-generated outputs are factual and reliable, addressing risks like hallucinations or contextual inaccuracies. High-quality data is essential for building accurate, reliable AI models. Data integration combines and cleans data from diverse sources, providing a robust foundation for training. Grounded AI models, built on this foundation, enhance performance through: Factual Accuracy: Keep your AI system grounded in reality by anchoring responses to factual information. This reduces the risk of generating hallucinations or misleading information. Contextual Relevance: Enable AI systems to consider the specific context of a query or request, leading to more relevant and accurate outputs. Ethical Considerations: Help mitigate biases and ensure that AI systems are aligned with ethical principles. By combining these elements, we can harness the power of AI while mitigating risks and ensuring that AI benefits society as a whole. Our Plan on Microsoft Learn titled “Implementing data integration and model grounding with Azure AI Foundry and Microsoft Fabric” includes foundational instruction for building for building accurate and responsible AI systems as your team begins the process of creating advanced AI solutions. Get expert AI guidance at our Microsoft Learn Challenge Looking to deepen your understanding of Responsible and Trustworthy AI with help from our industry experts? Running now through Jan. 10, 2025, the Microsoft Learn Challenge: Ignite Edition is a free, eight-week program offering interactive events, expert guidance, and exclusive training materials on cutting-edge technologies like Microsoft Azure AI, Microsoft Fabric, and Data Security. Participants master skills like data integration, AI model grounding, and responsible AI practices, earning a digital badge upon completion to showcase their expertise. Don’t miss this chance to advance your AI knowledge and lead the way in ethical AI innovation. Register today to begin your journey! Watch Microsoft experts put ethical AI principles into practice The ultimate impact of building advanced AI solutions hinges on trust, which ultimately yields higher growth potential and stronger customer loyalty. Our brand-new episode of the Azure Enablement Show, titled Trustworthy AI: From Principles to Practice, explores our own dedication to Responsible and Trustworthy AI principles and demonstrates our new AI content safety features. The video also gives a detailed overview of the two Plans on Microsoft Learn, introduced in this blog, that are designed to help developers gain the skills needed to implement these principles in their own AI projects. Embracing Responsible AI to stay ahead in the digital age As the demand for ethical AI solutions rises, Responsible and Trustworthy AI frameworks are essential for businesses that aim to lead in today’s AI market. Trustworthy AI focuses on creating secure, reliable systems, while Responsible AI considers societal impact and ethical alignment. Together, these frameworks provide a comprehensive approach to building AI systems that users, regulators, and stakeholders can trust. By exploring resources like our official Plans on Microsoft Learn, Implementing data integration and model grounding with Azure AI Foundry and Microsoft Fabric and Evolve with gen AI: Operationalize your Azure gen AI solutions with fine-tuning and prompt flow, plus our Trustworthy AI: From Principles to Practice Azure Enablement Show video, you can stay ahead in an ever-expanding field, building AI solutions that meet modern standards for reliability and ethics.Accelerate your AI journey and discover new resources at Microsoft Ignite 2024
Unlock the future of AI development at Microsoft Ignite 2024! 🚀 Explore groundbreaking tools like Azure AI Foundry, GitHub Copilot for Azure, and GenAIOps. Build intelligent, scalable applications faster with unified SDKs, pre-built templates, and the OpenAI .NET library. Whether you're enhancing workflows, managing models, or diving into generative AI, Ignite offers innovative resources to accelerate your journey. Connect with experts, explore solutions, and gain insights into building trusted AI. Dive in and start redefining your AI goals today!Essentials for building and modernizing AI apps on Azure
Building and modernizing AI applications is complex—but Azure Essentials simplifies the journey. With a structured, three-stage approach—Readiness and Foundation, Design and Govern, Manage and Optimize—it provides tools, best practices, and expert guidance to tackle key challenges like skilled resource gaps, modernization, and security. Discover how to streamline AI app development, enhance scalability, and achieve cost efficiency while driving business value. Ready to transform your AI journey? Explore the Azure Essentials Hub today.Unlocking the Best of Azure with AzureRM and AzAPI Providers
With the recent release of AzAPI 2.0, Azure offers two powerful Terraform providers to meet your infrastructure needs: AzureRM and AzAPI. The key question is, when should you use each one? This article offers a clear guide for Terraform users, particularly those familiar with the AzureRM provider, on some ideal scenarios for each. The recommendations provided within this post are jointly provided between HashiCorp and Microsoft; click here for HashiCorp's blogpost.
