Deploying task-specific machine learning models using synthetic weights

A non-transitory computer-readable storage medium coupled to one or more processors and having instructions stored thereon which, when executed by the one or more processors, cause the one or more processors to perform operations for deploying machine learning (ML) models for inference in production environments, the operations comprising:

• providing a ML model using a set of training data, the ML model having a set of weights associated therewith;
• generating a latent representation of the set of weights by inputting the set of weights into an encoder of a variational autoencoder (VAE);
• generating a denoised latent representation based on conditioning text by:
• diffusing the latent representation to generate a noisy latent representation, and
• denoising the noisy latent representation to provide the denoised latent representation;
• providing a reconstructed set of weights by inputting the denoised latent representation into a decoder of the VAE, the decoder outputting the reconstructed set of weights;
• and
• deploying an updated ML model for production inference.

A system, comprising:

• a computing device;
• and
• a computer-readable storage device coupled to the computing device and having instructions stored thereon which, when executed by the computing device, cause the computing device to perform operations for deploying machine learning (ML) models for inference in production environments, the operations comprising:
• providing a ML model using a set of training data, the ML model having a set of weights associated therewith;
• generating a latent representation of the set of weights by inputting the set of weights into an encoder of a variational autoencoder (VAE);
• generating a denoised latent representation based on conditioning text by:
• diffusing the latent representation to generate a noisy latent representation, and
• denoising the noisy latent representation to provide the denoised latent representation;
• providing a reconstructed set of weights by inputting the denoised latent representation into a decoder of the VAE, the decoder outputting the reconstructed set of weights;
• and
• deploying an updated ML model for production inference.