On-Device Machine Learning In Android: Frameworks and Ecosystem

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On-device machine learning refers to inference/training of ML-models which occurs completely on the device, without network calls or servers. On-device ML has its own pros and cons when compared to server-side inference. Major ML frameworks like TensorFlow, PyTorch have utilities to deploy models in Android apps, while services like Mediapipe and MLKit provide cleaner APIs for common ML-tasks. In my latest blog, we'll explore on-device ML libraries and utilities which can help Android developers deploy ML-models on-device.


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