Abstract
Endpoint management solutions cannot be utilized to manage on-device machine learning models. This disclosure describes techniques to integrate the best-in-class capabilities of cloud ML model management and endpoint management to enable lifecycle management and security of on-device ML model deployments. Endpoint management solutions as described herein include the capability to manage on-device models, e.g., to perform tasks such as model tracking, upgrade, and wipe out compliance. The described techniques, which can be implemented as part of an endpoint management solution, use a model catalog to track device deployments for a given machine learning model. When a model upgrade is available, a notification is provided to administrators, app developers, etc. On-device models can be upgraded, deleted, and tracked independent of app deployment. Additionally, with user permission, observability of on-device models is enabled through endpoint management to detect misuse. The endpoint management solution and model catalog also provide a deployment view of on-device models, including model versions and can be used to ensure compliance.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Recommended Citation
Bhaskar S, Hari, "Lifecycle Management and Security of On-device Machine Learning Models", Technical Disclosure Commons, (July 11, 2023)
https://www.tdcommons.org/dpubs_series/6039