TensorFlow is a tool to implement machine learning developed by Google, and was open sourced in 2015. It is a product that can be installed on desktops and can be used to create machine learning models. Once the model has been built and trained on the desktop, the developer can transfer these models to mobile devices and start using them to predict results in mobile applications by integrating them into iOS and Android mobile applications. There are currently two flavors of TensorFlow available for implementing machine learning solutions on mobile and embedded devices:
- Mobile devices: TensorFlow for Mobile
- Mobile and Embedded devices: TensorFlow Lite
The following table will help you to understand the key differences between TensorFlow for mobile and TensorFlow Lite:
|
TensorFlow for Mobile |
TensorFlow Lite |
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Designed to work with larger devices. |
Designed to work with really small devices. |
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Binary is optimized for mobile. |
Binary is really very small in size optimized for mobile and embedded devices, minimal dependencies, and enhanced performance. |
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Enables deployment in CPU, GPU, and TPU across Android, iOS, and Raspberry Pi. |
Supports hardware acceleration. Deployment possible on iOS, Android, and Raspberry Pi. |
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Recommended for usage now in mobile devices for production deployments. |
Still under Beta and is undergoing improvements. |
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Wider operator and ML model support available. |
Limited operators supported, and not all ML models are supported. |