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 apps

This 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.