Abstract: A method includes obtaining time-series data of at least one type and at least one textual description of the at least one type of time-series data and processing the time-series data and the at least one textual description using a foundation machine learning model. Processing the time-series data and the at least one textual description using the foundation machine learning model includes generating at least one embedding of the at least one textual description, combining the time-series data and the at least one embedding of the at least one textual description to generate combined data, and generating embedding vectors using the combined data.

Abstract: Machine learning model optimization explainability application is provides explanations (e.g., natural language explanations) for the operations and decisions associated with an optimization model (e.g., optimization algorithm) used to solve an optimization problem. More specifically, the software application for machine learning model optimization explainability enables explainability for a query that determine how the solution was generated. The system can provide a query response (e.g., natural language explanation), and perform a variety of different actions to address any issues surfaced in the query response.