System and method for optimizing multi-domain machine learning models

A method for optimizing multi-domain machine learning models, comprising:

• causing, by one or more processors, a meta-model to determine layer-specific precision levels for each neural network layer of a plurality of neural network layers of a trained machine learning model based on measured output activation values produced by each neural network layer during forward propagation with a validation dataset;
• assigning, by the one or more processors, the determined layer-specific precision levels to each neural network layer according to a predetermined priority order to create a quantized machine learning model;
• training, by the one or more processors, a meta-learning model using the trained machine learning model to predict software library configurations;
• utilizing, by the one or more processors, the trained meta-learning model to identify a predefined set of software libraries required for operation based on hardware specifications of a target device and specific tasks to be executed on the target device;
• dynamically linking, by the one or more processors, the identified predefined set of software libraries with the quantized machine learning model, wherein the quantized machine learning model with the dynamically linked predefined set of software libraries is deployed on the target device to enable autonomous operation of the target device.