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!Getting secrets from Key Vault in YAML pipeline
If you have ever created an Azure App Service or Azure Function App that uses app settings, then you have dealt with the problem of how you are going to get those settings secure and updated correctly in each environment. You need a secure location to store this information and then be able to access it during your deployment process. Azure Key Vault and using the Azure Key Vault task inside a deployment pipeline in Azure DevOps can solve this problem for you. If you prefer video, then have a look at this as it will walk you through the steps of getting this setup.Microsoft Startup Spotlight Live Series
As a founder, developer, or AI enthusiast, have you ever wondered what AI startups are solving, the technologies they use, or the journeys their founders have taken? How do they tackle challenges and create impactful solutions? These are questions we often ask, and there’s no better way to find answers than by hearing directly from the innovators themselves. I’m excited to introduce the Startup Spotlight Series—a bi-weekly live event hosted on the Microsoft Reactor YouTube channel. This series offers a unique opportunity to: Hear exclusive stories from AI startup founders. See live demonstrations of their technologies. Learn about the latest AI trends and real-world solutions for customers’ challenges 💡 Why Should You Tune In? The Startup Spotlight Series isn’t just another livestream—it’s a unique opportunity designed to provide insights, inspiration, and practical value for a variety of audiences: Startups Founders: Learn from the successes (and struggles) of AI startup leaders. Gain actionable insights into building innovative products, scaling businesses, and navigating challenges. Developers: Dive into the technical details with live product demos and behind-the-scenes discussions. See how their solutions are being built and deployed. Students: Discover the latest AI trends and practical applications. Discover how startups are leveraging AI to create impactful solutions. Whether you're looking for inspiration or considering a future in AI, this series is packed with insights to help you grow. AI Enthusiasts: Stay ahead of the curve by learning how startups are revolutionizing industries. From transformative use cases to the stories behind the technologies, you’ll leave every episode inspired. Have burning questions about AI? Each session includes a live Q&A, where you can ask founders directly about their journeys, challenges, and advice. This is your chance to engage with the people shaping the future of AI! Thursday Jan 30 2025, Startup Spotlight: Activeloop Join us for the second episode of the Startup Spotlight Live Series, a space where AI startups share their journeys, showcasing their innovative technologies and demonstrating live product use cases. In this episode, Gabriela de Queiroz sits down with Davit Buniatyan from Activeloop to explore how they’re leveraging Microsoft to grow, scale, and address real-world challenges. Thursday Feb 13th 2025, Startup Spotlight: Synthesized Join us for the third episode of the Startup Spotlight Live Series, a space where AI startups share their journeys, showcasing their innovative technologies and demonstrating live product use cases. In this episode, Gabriela de Queiroz sits down with Nicolai Baldin from Synthesized to explore how they’re leveraging Microsoft to grow, scale, and address real-world challenges. Thursday Feb 27th 2025, Startup Spotlight: Dataloop Join us for the fourth episode of the Startup Spotlight Live Series, a space where AI startups share their journeys, showcasing their innovative technologies and demonstrating live product use cases. In this episode, Gabriela de Queiroz sits down with Dataloop to explore how they’re leveraging Microsoft to grow, scale, and address real-world challenges. Join Us If you’re as excited about the future of AI as I am, I encourage you to tune in to the Startup Spotlight Series. Episodes air every other Thursday at 10:00 AM Pacific Time on the Microsoft Reactor YouTube channel. Can’t make it live? Don’t worry—episodes will be recorded and available for you to watch later at your convenience at Microsoft Reactor Youtube Sign up now to receive reminders and updates for upcoming episodes: aka.ms/startup-spotlight 🚀' But that's not all... Startups can apply (aka.ms/ss-credits) and get tons of benefits: Up to $150,000 in Azure credits Free access to leading AI models through Azure, including OpenAI's models, Llama 2 from Meta, and more. 1:1 meetings with experts who can help solve immediate business challenges, plus provide technical guidance on the latest in AI. 30+ additional free and discounted tools, tech, and services from Microsoft and our partners including M365, GitHub, LinkedIn Premium and more. 👋 See you there!Make your own private ChatGPT
Introduction Creating your own private ChatGPT allows you to leverage AI capabilities while ensuring data privacy and security. This guide walks you through building a secure, customized chatbot using tools like Azure OpenAI, Cosmos DB and Azure App service. Why Build a Private ChatGPT? With the rise of AI-driven applications, organizations, people often face challenges related to data privacy, customization, and integration. Building a private ChatGPT addresses these concerns by: Maintaining Data Privacy: Keep sensitive information within your infrastructure. Customizing Responses: Tailor the chatbot’s behavior and language to suit your requirements. Ensuring Security: Leverage enterprise-grade security protocols. Avoiding Data Sharing: Prevent your data from being used to train external models. If organizations do not take these measures their data may go into future model training and can leak your sensitive data to public. Eg: Chatgpt collects personal data mentioned in their privacy policy Prerequisites Before you begin, ensure you have: Access to Azure OpenAI Service. A development environment set up with Python. Basic knowledge of FastAPI and MongoDB. An Azure account with necessary permissions. If you do not have Azure subscription, try Azure for students for FREE. Step 1: Set Up Azure OpenAI Log in to the Azure Portal and create an Azure OpenAI resource. Deploy a model, such as GPT-4o (multimodal), and note down the endpoint and API key. Note there is also an option of keyless authentication. Configure permissions to control access. Step 2: Use Chatgpt like app sample You can select any repository to be as base template for your app, in this I will be using the third option AOAIchat. It is developed by me. GitHub - mckaywrigley/chatbot-ui: AI chat for any model. Azure-Samples/azure-search-openai-demo: A sample app for the Retrieval-Augmented Generation pattern running in Azure, using Azure AI Search for retrieval and Azure OpenAI large language models to power ChatGPT-style and Q&A experiences. sourabhkv/AOAIchat: Azure OpenAI chat This architecture diagram represents a typical flow for a private ChatGPT application with the following components: App UX (User Interface): This is the front-end application (mobile, web, or desktop) where users interact with the chatbot. It sends the user's input (prompt) and displays the AI's responses. App Service: Acts as the backend application, handling user requests and coordinating with other services. Functions: Receives user inputs and prepares them for processing by the Azure OpenAI service. Streams AI responses back to the App UX. Reads from and writes to Cosmos DB to manage chat history. Azure OpenAI Service: This is the core AI service, processing the user input and generating responses using models like GPT-4o. The App Service sends the user input (along with context) to this service and receives the AI-generated responses. Cosmos DB: A NoSQL database used to store and manage chat history. Operations: Writes user messages and AI-generated responses for future reference or analysis. Reads chat history to provide context for AI responses, enabling more intelligent and contextual conversations. Data Flow: User inputs are sent from the App UX to the App Service. The App Service forwards the input (with additional context, if needed) to Azure OpenAI. Azure OpenAI generates a response, which is streamed back to the App UX via the App Service. The App Service writes user inputs and AI responses to Cosmos DB for persistence. This architecture ensures scalability, secure data handling, and the ability to provide contextual responses by integrating database and AI services. What can you do with my template? AOAIchat supports personal, enterprise chat enabled by RAG People can enable RAG mode if they want to search within their database, else it behaves like normal ChatGPT. It supports multimodality, (supports image, text input) also depends on model deployed in Azure AI foundry. Step 3: Deploy to Azure Deploy a Cosmos DB account in nearest region Deploy Azure OpenAI model (gpt-4o, gpt-4o-mini recommended) Deploy Azure App service, try using container I would recommend B1plan to your nearest region, select docker registry sourabhkv/aoaichatdb:0.1 startup command uvicorn app:app --host 0.0.0.0 --port 80 After app service starts, put all environment variables The application requires the following environment variables to be set for proper configuration: Environment Variable Description AZURE_OPENAI_ENDPOINT The endpoint for Azure OpenAI API. AZURE_OPENAI_API_KEY API key for accessing Azure OpenAI. DEPLOYMENT_NAME Azure OpenAI deployment name. API_VERSION API version for Azure OpenAI. MAX_TOKENS Maximum tokens for API responses. MONGO_DETAILS MongoDB connection string. AZURE_OPENAI_ENDPOINT=<your_azure_openai_endpoint> AZURE_OPENAI_API_KEY=<your_azure_openai_api_key> DEPLOYMENT_NAME=<your_deployment_name> API_VERSION=<your_api_version> MAX_TOKENS=<max_tokens> MONGO_DETAILS=<your_mongo_connection_string> Optional feature: implement authentication to secure access. Within app service select Authentication and select service providers. I went with Entra based authentication with single tenant. There is option of multi-tenant, personal accounts as well. Restart App service and within 2 minutes your private ChatGPT is ready. Pricing Pricing may depend on the plan you have deployed resources and region. Check Azure calculator for price estimation. My estimate for pricing I deployed all my resources in Sweden central Cosmos DB config - Cosmos DB for MongoDB (RU) serverless config with single write master, 2 GB transactional storage, 2 backup plan (FREE) ~ 0.75$ Azure OpenAI service - plan S0, model gpt-4o-mini global deployment, Input 20000 tokens, Output 10000 tokens ~ 9.00$ App service plan - OS Linux, Tier B1, instance count 1 ~13.14$ Total monthly cost = 22.89$ This price may vary in future, in region I calculated my configuration in Azure calculator Governance Azure OpenAI provides content filters to block any kind of input that violates responsible AI practices. Categories include Hate and Fairness Sexual Violence Self-harm User Prompt Attacks (direct and indirect) The content filtering system detects and takes action on specific categories of potentially harmful content in both input prompts and output completions. Azure OpenAI Service includes default safety settings applied to all models set as medium. Content filters can be modified to different level depending on use case. It supports RAG, I have provided detailed solution for it in my GitHub. Practical implementation GE Aerospace, in partnership with Microsoft and Accenture, has launched a company-wide generative AI platform, leveraging Microsoft Azure and Azure OpenAI Service. This solution aims to transform asset tracking and compliance in aviation, enabling quick access to maintenance records and reducing manual processing time from days to minutes. It supports informed decision-making by providing insights into aircraft leasing, compliance gaps, and asset health. For enterprises implementing private ChatGPT solutions, this illustrates the potential of generative AI for streamlining document-intensive processes while ensuring data security and compliance through cloud-based infrastructure like Azure. GE Aerospace Launches Company-wide Generative AI Platform for Employees | GE Aerospace News Build your own private ChatGPT style app with enterprise-ready architecture - By Microsoft Mechanics How to make private ChatGPT for FREE? It can be FREE if all of the setup is running locally on your hardware. Cosmos DB <-> MongoDB. Azure OpenAI <-> Ollama / LM studio Refer this NOTE : I have used gpt-4o, gpt-4o-mini these values are hardcoded in webpage, if you are using other models, you might have to change them in index.html. App Service <-> Local machine Register for Github models to access API for FREE. Note: GitHub models have rate limit for different models. Useful links sourabhkv/AOAIchat: Azure OpenAI chat What is RAG? Get started with Azure OpenAI API Chat with Azure OpenAI models using your own dataEssentials 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.Enhancing Data Security and Digital Trust in the Cloud using Azure Services.
Enhancing Data Security and Digital Trust in the Cloud by Implementing Client-Side Encryption (CSE) using Azure Apps, Azure Storage and Azure Key Vault. Think of Client-Side Encryption (CSE) as a strategy that has proven to be most effective in augmenting data security and modern precursor to traditional approaches. CSE can provide superior protection for your data, particularly if an authentication and authorization account is compromised.Using Keycloak with Azure AD to integrate AKS Cluster authentication process
Integrating Azure Kubernetes Service (AKS) with Keycloak through Azure Active Directory (Azure AD) as an intermediary leverages Azure AD’s support for OpenID Connect (OIDC) to handle authentication and authorization. This integration enhances security, streamlines user management, and simplifies the authentication process for users accessing the AKS cluster.
