TFLite
TensorFlow Lite, now known as LiteRT, is a lightweight framework and runtime for deploying and executing machine-learning models on mobile, embedded, and edge devices.
Deep Learning with Python, Third Edition
A framework for efficient on-device TensorFlow inference on mobile and embedded hardware.
- How it is used
- Deploys TensorFlow inference on phones and embedded hardware when privacy, latency, or connectivity rules out remote serving.
- How deep it goes
- Defines the deployment role and supported hardware, but stops short of explaining TFLite runtime internals or conversion details.
- Examples
- Uses smartphone spam detection and factory bad-cookie detection to motivate local inference.
- What you can do after
- Operate on-device inference by exporting a Keras model and selecting TFLite for supported mobile or embedded targets.
Read how Deep Learning with Python, Third Edition explains TFLite →
Deep Learning with R, Third Edition
TensorFlow Lite is a framework for running machine-learning inference efficiently on mobile, embedded, and resource-constrained devices.
- How it is used
- Deploys machine-learning inference locally when privacy, latency, connectivity, or device constraints make remote serving unsuitable.
- 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 you can do after
- Deploys a saved Keras model for inference on supported mobile, embedded, ARM, Raspberry Pi, or microcontroller devices.
Read how Deep Learning with R, Third Edition explains TFLite →
Related concepts
- Optimumproduced by · Optimum is a Hugging Face library for optimizing and deploying machine-learning models across inference engines and hardware platforms.
- TensorFlowpart of · TensorFlow is an open-source software platform for numerical computation and machine learning, with tools for developing, training, and deploying models.
- TensorFlow Servinguses · TensorFlow Serving is an open-source system for deploying trained machine-learning models and exposing them through production inference APIs.
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