Building management system for implementing machine-learning on edge devices

A method, comprising:

• receiving, by one or more processors coupled to non-transitory memory, optimization criteria to optimize a first machine-learning model for a target platform, the first machine-learning model having one or more model parameters;
• transforming, by the one or more processors, based on the target platform and the optimization criteria, at least one datatype of the one or more model parameters of the first machine-learning model to generate a second machine-learning model;
• determining, by the one or more processors, using a verification dataset, an accuracy of the second machine-learning model;
• and
• retraining, by the one or more processors, the second machine-learning model using a training dataset responsive to the accuracy being less than a predetermined threshold.

A non-transitory computer-readable medium with instructions embodied thereon that, when executed by one or more processors, cause the one or more processors to perform operations comprising:

• receiving optimization criteria to optimize a first machine-learning model for a target platform, the first machine-learning model having one or more model parameters;
• transforming, based on the target platform and the optimization criteria, at least one datatype of the one or more model parameters of the first machine-learning model to generate a second machine-learning model;
• determining, using a verification dataset, an accuracy of the second machine-learning model;
• and
• retraining the second machine-learning model using a training dataset responsive to the accuracy being less than a predetermined threshold.