January 2025
Intermediate to advanced
698 pages
16h 48m
English
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8.2 Introduction to TensorFlow Lite and ONNX for Edge Devices
The rapid advancement of edge computing has revolutionized the deployment of machine learning models across a wide array of devices, including smartphones, tablets, wearables, and IoT devices. This shift towards edge-based AI presents both opportunities and challenges, as these devices typically have constraints in terms of computational resources, memory capacity, and power consumption that are not present in cloud-based infrastructures.
To address these limitations and enable efficient AI at the edge, specialized frameworks such as TensorFlow Lite (TFLite) and ONNX (Open Neural Network Exchange) have emerged. These powerful tools provide developers with the means to optimize, convert, ...
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