Implementing scalable storage of personalized machine learning models

A system for implementing scalable storage of personalized machine learning models, comprising:

• at least one processor;
• and
• at least one memory communicatively coupled to the at least one processor and comprising computer-readable instructions that upon execution by the at least one processor cause the at least one processor to perform operations comprising:
• generating a plurality of personalized machine learning models based on finetuning a base machine learning model, wherein each of the plurality of personalized machine learning models corresponds to a particular user from a plurality of users, wherein the base machine learning model comprises a first set of layers, and wherein each of the plurality of personalized machine learning models comprises a second set of layers;
• generating a plurality of difference models by computing differences between the first set of layers and the second set of layers, wherein the plurality of difference models correspond to the plurality of personalized machine learning models, respectively;
• processing the plurality of difference models by compressing parameters of each of the plurality of difference models to generate a plurality of compressed models;
• and
• storing the plurality of compressed models for future use of the plurality of personalized machine learning models, wherein the plurality of compressed models minimize storage costs without affecting performance quality of the plurality of personalized machine learning models.

A non-transitory computer-readable storage medium, storing computer-readable instructions that upon execution by a processor cause the processor to implement operations comprising:

• generating a plurality of personalized machine learning models based on finetuning a base machine learning model, wherein each of the plurality of personalized machine learning models corresponds to a particular user from a plurality of users, wherein the base machine learning model comprises a first set of layers, and wherein each of the plurality of personalized machine learning models comprises a second set of layers;
• generating a plurality of difference models by computing differences between the first set of layers and the second set of layers, wherein the plurality of difference models correspond to the plurality of personalized machine learning models, respectively;
• processing the plurality of difference models by compressing parameters of each of the plurality of difference models to generate a plurality of compressed models;
• and
• storing the plurality of compressed models for future use of the plurality of personalized machine learning models, wherein the plurality of compressed models minimize storage costs without affecting performance quality of the plurality of personalized machine learning models.