Custom Object Detection with Azure AI Vision and Python

Computer vision is transforming how we interact with technology, but deploying custom models can seem daunting. This course simplifies the process of training, publishing, and testing custom object detection models using Azure AI Vision. Through clear, step-by-step written explanations and code examples, you will transition from understanding core computer vision concepts to deploying operational models. You will learn how to prepare training datasets, evaluate model performance with modern metrics, publish your models to the cloud, and run testing scripts using the Python SDK. What you'll learn: Understand core computer vision terminology, image tagging principles, and model training fundamentals; Configure Azure AI Vision resources and set up your Python development environment; Publish trained custom object detection models to secure cloud endpoints; Test published models by writing Python scripts to send prediction requests and parse JSON responses; Evaluate model performance using key metrics like precision, recall, and mean Average Precision (mAP); Apply best practices for API authentication, error handling, and resource management. The course begins with foundational concepts of object detection and cloud architecture. You will then progress through step-by-step written guides that show you how to manage your Azure resources, publish your custom model, and write Python code to run inference on new images. This text-only course is designed for beginner developers, data enthusiasts, and cloud learners who want to explore computer vision. No prior machine learning experience is required, though a basic familiarity with Python is helpful. Start reading today to bring intelligent image analysis to your Python applications.