Enabling Real Time Machine Learning Operations for Transient Science (MLOps)

To address NASA's lack of MLOps for transient science, this project will develop and demonstrate an MLOps framework with generative AI and explainable AI (XAI) to autonomously filter transient events for the Roman High Latitude Survey, with the ultimate goal of transferring this capability to other NASA missions.

This proposal outlines a reusable MLOps framework for transient science that will not only enhance the Roman Space Telescope's objectives but also transfer this capability to other missions like PRIMA, UVEX, and those related to the Moon to Mars initiative. The framework incorporates state-of-the-art generative AI and explainable AI (XAI), which are crucial for transitioning models from simulations to real-world observations during a mission's first year and for providing support when operational anomalies occur.

This is a mature technology (TRL 7+) — the realistic path in is usually NASA's Technology Transfer Program: licensing an existing NASA patent, or a Space Act Agreement to use NASA facilities/expertise directly. NASA also runs a startup licensing program with no upfront fee for companies formed to commercialize a specific NASA technology.

None of these are guaranteed paths for this specific project — TechPort itself doesn't have an "apply" button. Reaching out to the contact(s) above with a specific question is usually the fastest way to find out what's actually open.