AI-3004 Build an Azure AI Vision solution

In this lecture, you’ll learn how to harness Azure AI Vision to extract meaningful insights from images. We’ll explore the built-in capabilities of the service, including detecting objects, identifying visual features, and generating descriptive tags and captions automatically. You’ll also understand how image analysis can be applied across industries—from retail product categorization to medical image support and content moderation.

We’ll walk through the key concepts of image analysis:

Extracting tags, categories, and descriptions.

Understanding scene and content recognition.

Identifying dominant colors and image properties.

Leveraging pre-trained models for quick deployment.

In this hands-on lab, you’ll put your learning into practice by working directly with Azure AI Vision’s image analysis capabilities. Using the Azure AI SDK and portal, you’ll learn how to send images to the service and interpret the structured output it provides.

During this lab, you’ll:

Upload or reference sample images for analysis.

Generate tags, categories, and captions automatically.

Examine the JSON output to understand how Azure structures visual insights.

Explore use cases such as product tagging, scene detection, and content filtering.

This exercise is designed to give you practical experience so that you don’t just understand the theory, but also feel confident in integrating image analysis into your own AI-powered applications.

By the end of this lab, you’ll be able to:

Run end-to-end image analysis tasks.

Interpret and utilize AI-generated metadata.

Identify scenarios where AI-driven image understanding adds value

In this lecture, we dive into one of the most powerful capabilities of Azure AI Vision — extracting text from images using Optical Character Recognition (OCR).

You’ll learn how Azure AI can detect and read both printed and handwritten text from various types of images, whether it’s a scanned document, a photo of a street sign, or handwritten notes. We’ll explore how this functionality powers real-world applications such as digitizing documents, automating data entry, and enabling accessibility tools.

Key concepts covered in this lecture include:

How OCR works in Azure AI Vision.

Reading text in different languages and formats.

Understanding bounding boxes and layout information for accurate text positioning.

Using OCR results in downstream applications such as search indexing or natural language processing.

In this hands-on lab, you’ll apply what you’ve learned about Azure AI Vision’s text recognition capabilities. The focus is on using OCR (Optical Character Recognition) to extract printed and handwritten text from images.

You’ll walk through practical exercises, including:

Uploading an image and calling the Read API.

Extracting text results and reviewing bounding boxes around recognized words.

Testing the service with different image types, such as scanned receipts, street signs, and handwritten notes.

Understanding how to handle multi-language text and formatted layouts.

In this lecture, we dive into Azure AI Vision’s facial analysis capabilities. You’ll learn how to detect human faces in images, analyze attributes, and understand the different use cases for this powerful feature.

Key topics covered include:

Face Detection – identifying one or multiple faces in an image.

Facial Attributes – analyzing details such as head pose, age estimation, emotion, and facial landmarks.

Face Recognition – understanding how to match and verify identities between two images.

Security & Ethics – exploring Microsoft’s responsible AI principles for using face recognition safely and fairly.

We’ll also walk through industry use cases such as identity verification, access control, personalized experiences, and sentiment analysis.

In this lecture, you’ll explore how to train and use image classification and object detection models with Azure AI Vision. These capabilities allow applications to not only recognize what an image contains but also locate specific objects within it.

Key topics covered include:

Image Classification – assigning a label or category to an image (e.g., cat, dog, car).

Object Detection – identifying and locating multiple objects in an image with bounding boxes.

Pre-Built vs. Custom Models – when to use Azure’s pre-trained models versus training a custom model with your own dataset.

Real-World Scenarios – applying classification and detection in retail (inventory tracking), manufacturing (defect detection), and healthcare (image diagnostics).

In this hands-on lab, you’ll apply what you learned in the previous lecture by building and training a custom image classification model in Azure AI Vision. Unlike pre-built models, custom models let you define your own categories and train the model with labeled images that match your business needs.

In this lab, you’ll practice:

Uploading and labeling training data for your custom categories.

Training the model using Azure AI Vision’s no-code/low-code interface.

Testing the model to evaluate accuracy and performance.

Deploying the model as an endpoint, so it can be integrated into applications.

Running predictions to classify new images based on your trained categories.

In this lecture, you’ll take computer vision to the next level by integrating Azure AI Vision with generative AI capabilities. Instead of simply analyzing or classifying images, you’ll learn how to create applications that can understand visual input and generate intelligent, context-aware responses.

We’ll cover:

Combining vision with generative AI models to enhance user experiences.

Using image inputs as prompts for AI responses.

Building intelligent chat-style applications that can describe images, answer questions about visual content, and even guide users through decisions.

Best practices for ensuring responsible AI use, including content filtering and ethical considerations.

In this hands-on lab, you’ll bring together everything you’ve learned so far to build a fully functional vision-enabled chat application powered by Azure AI Vision and generative AI models.

You’ll work step by step to:

Connect Azure AI Vision services with a chat-based interface.

Allow users to upload or share images within the chat and receive intelligent, AI-generated responses.

Implement features such as image captioning, contextual Q&A about images, and natural language-driven insights.

Explore how to integrate Azure OpenAI Service for multimodal experiences.

Apply responsible AI practices by incorporating content moderation and safe deployment configurations.

By completing this lab, you will have created a practical AI-powered application that demonstrates the potential of multimodal solutions. This project reflects real-world use cases, from customer service assistants that interpret product images, to accessibility apps that help visually impaired users interact with their environment.

In this lecture, you’ll explore how to use Azure AI Vision and Azure OpenAI services to generate entirely new images from text prompts. This is where Generative AI meets computer vision, enabling you to create high-quality visuals on demand.

You’ll learn how to:

Write effective text prompts to guide AI image generation.

Understand how the Azure AI Vision SDK integrates with generative models.

Generate different types of images, from simple objects to complex scenes.

Apply best practices for responsible AI image generation, including avoiding bias and ensuring ethical use.