8.3 Deploying Models to Mobile and Edge Devices
Deploying machine learning models to mobile and edge devices involves a comprehensive process that encompasses several critical stages, each playing a vital role in ensuring optimal performance and efficiency:
Model Optimization and Compression: This crucial step involves refining and compressing the model to ensure it operates efficiently on devices with constrained resources. Techniques such as quantization, pruning, and knowledge distillation are employed to reduce model size and computational demands while maintaining accuracy.
Framework Selection and Model Conversion: Choosing the appropriate framework, such as TensorFlow Lite or ONNX, is essential for converting and executing the model on the ...