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Sustainable Implementation of Machine Learning for Particle Accelerators

Oct 18, 2022, 9:15 AM
Oral Machine Learning & Reliability Machine Learning & Reliability


MICELI, Tia (Fermilab)


The deployment of Machine Learning (ML) applications in a production environment requires verification, validation, assurance, and trust. ML models are notoriously difficult to maintain in these environments where data and systems may evolve over time and long-term maintenance is required. The models require active management for (1) reproduction or replication of model weights, (2) monitoring data drift, (3) tracking model performance, and (4) updating models. A Machine Learning Operations (MLOps) framework that will ensure a sustainable develop-deploy-monitor paradigm for accelerator control systems will be presented along with an overview of R&D to enable ML capabilities for accelerator operations. The R&D is being initiated by the Accelerator Controls Operations Research Network (ACORN) DOE O413.3b project to modernize Fermilab’s accelerator control system in preparation for operations with megawatt particle beams.

Primary author

MICELI, Tia (Fermilab)

Presentation materials