Task leads: Sioni Summers (CERN)

For this task, we propose to research and develop methods to make optimal use of the information that is available in the trigger system, and a system to deploy models with robust provenance tracking and reproducibility. We anticipate that Machine Learning will be prevalent throughout the CMS L1T during the HL-LHC era, with around 20 models and 50 billion inferences per second already accounted for. Developing and operating the experiment with this large amount of ML in the data acquisition pipeline is a new frontier for CMS.