What are the best mobile app development tools for integrating AI and machine learning?

2 PyTorch Mobile

PyTorch Mobile is another open-source framework for developing and deploying ML models on mobile devices. PyTorch Mobile is based on PyTorch, a popular framework for creating dynamic neural networks. PyTorch Mobile enables you to use the same code and APIs as PyTorch to build and train ML models on your desktop or cloud, and then export them to mobile devices. PyTorch Mobile supports Android and iOS platforms, and offers features such as model optimization, quantization, and custom operators.

4 Core ML

Core ML is an Apple framework that enables you to integrate ML models into your iOS, iPadOS, macOS, watchOS, and tvOS apps. Core ML optimizes the performance and efficiency of your ML models on Apple devices, using features such as hardware acceleration, neural engine, and memory mapping. Core ML supports a variety of ML models, such as convolutional neural networks, recurrent neural networks, decision trees, and support vector machines. You can use Core ML Tools to convert models from other frameworks, such as TensorFlow, PyTorch, and Keras, into Core ML format.

5 React Native

React Native is a framework that allows you to build native mobile apps using JavaScript and React, a library for creating user interfaces. React Native enables you to use the same codebase for both Android and iOS platforms, reducing the development time and cost. React Native also supports AI and ML integration, using libraries such as react-native-tensorflow, react-native-firebase-mlkit, and react-native-core-ml. These libraries allow you to use TensorFlow Lite, ML Kit, and Core ML models in your React Native apps.