Isolated model deployments using shared base components

Abstract

Approaches presented herein relate to the performing of tasks involving sensitive user data in a multi-tenant resource environment. In particular, various approaches provide for the allocation of resources to perform tasks, such as inferencing tasks, using user-specific data and models deployed in isolated, single tenant environments. A set of base models, code, and artifacts can be maintained and updated as appropriate. When a user requests to perform an inferencing task, these base components can be deployed to a user-specific environment for performing the inferencing task. After the inferencing task has been performed, the model and associated components can be removed from the user-specific environment. In instances where a user might want to further train or fine-tune a model using user-specific data, offline training may be performed to generate a new, user-specific artifact or set of weights, which can subsequently be applied to the base components to effectively obtain a user-specific model.