Build AI-powered mobile apps that run locally-without depending on cloud APIs. This practical book teaches you how to convert Python AI models into ONNX format and run them inside . NET MAUI mobile apps using C# and ONNX Runtime. Written in a clear, beginner-friendly style, this guide helps developers bridge the gap between Python machine learning and cross-platform mobile app development. You will learn how to train simple AI models in Python, convert scikit-learn and PyTorch models to ONNX, test ONNX models with ONNX Runtime, and integrate them into . NET MAUI apps for Android, iOS, Windows, and macOS. Inside this book, you will learn how to:Create beginner-friendly AI models in PythonConvert scikit-learn models to ONNXExport PyTorch models to ONNXTest ONNX models before mobile deploymentCreate . NET MAUI apps for local AI inferenceLoad ONNX models from app resourcesRun predictions locally using C# and ONNX RuntimeBuild text, image, object detection, and business prediction appsImprove app performance and avoid UI freezingDebug common ONNX and . NET MAUI integration problemsPackage, publish, secure, and maintain local AI appsThis book includes step-by-step projects such as an Iris flower classifier, text classifier, image classifier, simple object detection app, sales prediction app, and a final capstone local AI project. Whether you are a Python developer who wants to deploy AI models to mobile apps, a C# developer exploring local AI, or a . NET MAUI learner interested in machine learning, this book gives you a practical path from model training to mobile deployment. Learn how to train in Python, convert to ONNX, and run AI locally in . NET MAUI.