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TFLite

As explained in Deep Learning with R, Third Edition

TensorFlow Lite is a framework for running machine-learning inference efficiently on mobile, embedded, and resource-constrained devices.

TFLite is a framework for efficient on-device machine-learning inference when sending data to a remote server is impractical because of privacy, latency, or connectivity constraints. section 6.3.2

Deploy TFLite when an application needs to:

  • Keep sensitive inputs on smartphones or embedded devices instead of sending them to a remote model. section 6.3.2
  • Avoid network round trips when strict latency requirements matter. section 6.3.2
  • Run inference across Android and iOS smartphones, ARM CPUs, Raspberry Pi, and certain microcontrollers. section 6.3.2

Private smartphone spam detection

A chat application can run its spam detection model on the user’s smartphone, preserving end-to-end-encrypted message content while avoiding remote inference latency. section 6.3.2

How Deep Learning with R, Third Edition covers it

How deep it goes
Defines the deployment role and supported device categories but does not detail conversion, optimization, or runtime implementation.
Examples
Uses smartphone spam detection and factory cookie flagging to illustrate privacy, latency, and hardware deployment choices.
What it assumes
Assumes a trained Keras model and introduces deployment as a choice between local devices and remote serving.
What you can do after
Deploys a saved Keras model for inference on supported mobile, embedded, ARM, Raspberry Pi, or microcontroller devices.
How it connects
Connects TensorFlow Lite with Keras, TensorFlow SavedModel, TensorFlow Serving, ONNX, and on-device inference.
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This explainer was generated by Manning's knowledge graph from the text of Deep Learning with R, Third Edition. The section links point to the parts of the book it draws on.