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.

Abstract: Systems and methods for automated analysis of one or more tables are provided. Particularly, a large language model is provided that analyzes a table (or multiple tables) and produces output information about the table without requiring the contents of the table to be provided as an input. For example, the large language model can output a natural language description of the table, description of the table contents, applications of the data in the table, privacy and security concerns, and/or any other types of relevant information. Additionally, a mechanism is provided that evaluates the outputs of the large language model for inaccurate or nonsensical natural language and provides an indication of the quality of the output to a user. The mechanism, for example, may include a combination of a hallucination filter and one or more metrics. The mechanism may also be used to filter the outputs of the large language model.