US 2024/0177049 A1Published application

Confidential tuning of pre-trained machine learning models

AssigneeAmazon Technologies, Inc.

InventorsLakshmi Naarayanan Ramakrishnan, Andrea Olgiati, Ankur Mehrotra, Karthik Gurumoorthy Subramanya Bharathy, Rakesh Ramakrishnan

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Abstract

Confidential tuning of pre-trained machine learning models may be provided. A request associated with a model user account to fine-tune a pre-trained machine learning model with model access restrictions may be received. The pre-trained machine learning model may be one of many pre-trained machine learning models uploaded for selection and fine-tuning. The pre-trained machine learning model may be further trained using a request specified data set, with the model access restrictions and access restrictions for the data set being enforced as part of the training. Then, the fine-tuned machine learning model may be made available for invocation by an application associated with the model user account without violating the model access restrictions and data access restrictions.

Claims20

Dependent claims

A system, comprising:

at least one processor;
and
a memory, storing program instructions that when executed by the at least one processor, cause the at least one processor to implement a machine learning service as part of a provider network, wherein the machine learning service is configured to:
receive a request associated with a model user account of the provider network to fine-tune a pre-trained machine learning model with model access restrictions uploaded in association with a model provider account of the provider network, wherein the request specifies a data set with data access restrictions for fine-tuning the pre-trained machine learning model;
load the pre-trained machine learning model, one or more tuning instructions specified by the provider of the pre-trained machine learning model, and the data set onto one or more computing resources provisioned by the machine learning service for performing the fine-tuning;
initiate training of the pre-trained machine learning model at the one or more computing resources according to the one or more tuning instructions and the data set to generate a fine-tuned machine learning model, wherein the model access restrictions and the data access restrictions are enforced at the one or more computing resources as part of the training;
store the fine-tuned machine learning model in a data store with the model access restrictions enforced;
provide an indication via an interface of the machine learning system that the fine-tune machine learning model is generated;
and
responsive to a request to deploy the fine-tuned machine learning model,
obtain the fine-tuned machine learning model from the data store;
and
deploy the fine-tuned machine learning model for invocation by an application associated with the model user account without violating the model access restrictions and the data access restrictions, wherein the invocation by the application is received via a network endpoint provisioned for the fine-tuned machine learning model by the machine learning service.

10 more elements

A method, comprising:

receiving, by a machine learning system, a request associated with a model user account to fine-tune a pre-trained machine learning model with model access restrictions uploaded in association with a model provider account, wherein the request specifies a data set with data access restrictions for fine-tuning the pre-trained machine learning model;
loading, by the machine learning system, the pre-trained machine learning model, one or more tuning instructions specified by the provider of the pre-trained machine learning model, and the data set onto one or more computing resources for performing the fine-tuning;
initiating, by the machine learning system, training of the pre-trained machine learning model at the one or more computing resources according to the one or more tuning instructions and the data set to generate a fine-tuned machine learning model, wherein the model access restrictions and the data access restrictions are enforced at the one or more computing resources as part of the training;
and
making, by the machine learning system, the fine-tuned machine learning model available for invocation by an application associated with the model user account without violating the model access restrictions and the data access restrictions.

2 more elements

One or more non-transitory, computer-readable storage media, storing program instructions that when executed on or across one or more computing devices cause the one or more computing devices to implement:

receiving, by a machine learning system, a request associated with a model user account to fine-tune a pre-trained machine learning model with model access restrictions uploaded in association with a model provider account, wherein the request specifies a data set with data access restrictions for fine-tuning the pre-trained machine learning model;
loading, by the machine learning system, the pre-trained machine learning model, one or more tuning instructions specified by the provider of the pre-trained machine learning model, and the data set onto one or more computing resources for performing the fine-tuning;
initiating, by the machine learning system, training of the pre-trained machine learning model at the one or more computing resources according to the one or more tuning instructions and the data set to generate a fine-tuned machine learning model, wherein the model access restrictions and the data access restrictions are enforced at the one or more computing resources as part of the training;
providing, by the machine learning system, an indication via an interface of the machine learning system that the fine-tune machine learning model is generated;
and
making, by the machine learning system, the fine-tuned machine learning model available for invocation by an application associated with the model user account without violating the model access restrictions and the data access restrictions.

3 more elements

Dependencies are inferred from the claim text.