How to bring your AI Model to Android devices

Google is providing developers with tools and technologies to bring their own AI models to Android-powered devices, enabling on-device AI and offering advantages such as reduced latency, enhanced privacy, cost savings, and less dependence on internet connectivity. On-device AI involves deploying and executing machine learning or generative AI models directly on hardware devices, instead of relying on cloud-based servers. The Google AI Edge platform provides a comprehensive ecosystem for building and deploying AI models on edge devices, supporting various frameworks and tools. To build custom AI features on Android, developers need to define their use case, choose the right tools and frameworks, and develop, train, and optimize their model. They can use MediaPipe Tasks for common solutions or create their own model using LiteRT, a lightweight runtime for deploying machine learning models. Developers can also use Model Explorer to understand and explore their model and accelerate model inference on Android by using GPU and NPU. Rigorous testing is crucial to ensure the model delivers the expected performance across various devices. Google is also emphasizing a "zero trust" approach, providing developers with tools to verify device integrity and user control over their data. The Play Integrity API is recommended for developers looking to verify their app, server requests, and the device environment. By leveraging these resources, developers can create cutting-edge applications that offer enhanced performance, privacy, and user experience.