ONNX AI for .NET MAUI Made Easy: Convert Python Models to and Run Local Inference in Mobile Apps with C#, Visual Studio 2026,

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 Python Convert scikit-learn models to ONNX Export PyTorch models to ONNX Test ONNX models before mobile deployment Create .NET MAUI apps for local AI inference Load ONNX models from app resources Run predictions locally using C# and ONNX Runtime Build text, image, object detection, and business prediction apps Improve app performance and avoid UI freezing Debug common ONNX and .NET MAUI integration problems Package, 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.

Pages: 784, Paperback, Independently published