Introduction Object detection is one of the most exciting use‑cases of computer vision, and the YOLO (You Only Look Once) family has become the go‑to solution for real‑time inference. In this tutorial you’ll learn how to: Train a custom YOLOv8 model on your own dataset. Wrap the model in a FastAPI service that accepts image uploads and returns detections instantly. Containerize the whole stack with Docker so it runs the same everywhere. Automate testing and deployment using a GitHub Actions CI/CD pipeline. By the end you’ll have a production‑ready API that can be deployed to any container host (AWS ECS, GCP Cloud Run, Azure Container Apps, or even your laptop). Tip: If you’re new to YOLOv8, the official Ultralytics repo ships with a very friendly CLI that handles most of the heavy lifting. We’ll use it as the foundation and then add a thin FastAPI wrapper around the exported model. 1.…