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Welcome to the Microsoft Certified Azure AI Engineer Associate (AI-102) course. This program is designed to build practical skills you can apply immediately—whether you’re preparing for the certification exam or implementing AI solutions in production. Azure is a dominant enterprise cloud platform, and its AI services power many real-world applications across industries. Below is a quick snapshot to set the stage.
A slide with a large blue "95%" and the caption "of Fortune 500 companies use Microsoft Azure," over a faint Microsoft Azure logo in the background.
Azure AI is used for customer support automation, real-time language translation, medical diagnostics, and more. Microsoft reports thousands of organizations relying on Azure AI for data analysis, model deployment, and delivering real-time business value—making this an ideal time to grow your Azure AI skills. I’m Hrithin Skaria, your instructor for this course. Over the lessons you’ll get a balance of conceptual guidance, hands-on demos, and exam-style questions to build competence and confidence.
A screenshot of a Microsoft Certified Azure AI Engineer Associate mock exam question about assigning a read-only role in Azure OpenAI Studio, with "Cognitive Services OpenAI User" selected and marked correct. A small circular inset in the lower-right shows a presenter speaking.
What you’ll learn first: core AI concepts and the Azure AI ecosystem. That foundation helps you choose the right services, design suitable architectures, and reason about trade-offs for real projects and exam scenarios.
A presentation slide titled "Azure Machine Learning" with a dark background, a central illustration of a robot, servers and two people working, and a short descriptive tagline. A small circular video overlay of a presenter appears in the bottom-right corner.
Next, we’ll cover core service areas in the sequence most engineers use them:
  • Computer vision with Azure AI Vision — image and video analysis, object detection, OCR, and face recognition.
  • Natural language processing — sentiment analysis, entity recognition, translation, and building conversational bots.
  • Generative AI with Azure OpenAI — large language models for summarization, content generation, and advanced conversational experiences.
  • Provisioning, security, endpoint management, cost control, and governance for Azure AI resources.
  • Knowledge mining with Azure AI Search — indexing documents and extracting structured data to make information searchable and actionable.
  • Automation with Azure AI Document Intelligence — extracting structured fields from forms and automating document-based workflows.
A presentation slide titled "The Face Service" explaining face detection, analysis, and recognition. It shows a diagram of a photo being processed by an AI/cloud icon and a circular video overlay of a presenter in the bottom-right.
A slide titled "Language Understanding" showing a three-step chat flow (User Interaction, Intent Recognition, Response Execution) with a mock chat interface connected to an AI model and external APIs. A small circular video of a presenter appears in the lower-right corner.
A presentation slide titled "Microsoft Certified Azure AI Engineer Associate" lists topics like Computer Vision, Natural Language Processing, and Generative AI on the left. On the right, a person wearing a KodeKloud t-shirt speaks in a studio with a brick-wall backdrop and bookshelf.
You’ll also learn how to provision and manage Azure AI resources, secure models and endpoints, and apply best practices for cost control and governance.
A screenshot of an Azure portal form for creating an OpenAI/Azure instance showing subscription, resource group, region, name, and pricing tier fields, with a validation error saying "The value must not be empty." A small circular video overlay of a presenter appears in the bottom-right corner.
Next: knowledge mining with Azure AI Search—techniques to index and query documents, images, and databases so insights are discoverable and actionable.
A presentation slide titled "Azure AI Search" showing an illustrated person beside a desktop screen with product thumbnails and a cloud search icon. There's also a small circular video inset of a presenter in the bottom-right.
Then we’ll cover automation with Document Intelligence to extract structured information from forms, speed up processing, and reduce human error in workflows.
A presentation slide titled "Document Intelligence Service" explaining that student data is auto-filled, with buttons labeled Name, Grades, Date of Birth, and ID Numbers plus illustrative graphics of people and a monitor. A small circular presenter video overlay appears in the bottom-right.
Throughout the course you’ll get hands-on labs, architecture guidance, and exam-focused tips. Participate in community forums to ask questions, collaborate on projects, and learn with peers—community learning speeds progress and keeps you motivated.
Study tip: Combine hands-on labs with the mock exams and architecture walkthroughs. Practical experience with Azure OpenAI, Cognitive Services, and Azure ML will improve your recall for exam scenarios and real-world deployments.
Course modules at a glance Links and references Are you ready to unlock the full potential of AI with Azure and take the next step in your career? Let’s begin this journey together and start transforming how you build intelligent solutions.

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