Abstract
Machine learning has shown significant potential for real-time monitoring, optimization, and control of scientific facilities. However, deploying and maintaining ML models in operational environments remains a substantial engineering challenge. Each facility presents unique data protocols, non-standard formats, and infrastructure constraints, forcing teams to rebuild integration pipelines for every new application.
We present SMOCS (Streaming Monitoring Optimization and Control System), a Kafka-based containerized framework that addresses this challenge through three contributions:
- Layered Abstraction: It provides a layered abstraction over Apache Kafka that separates infrastructure from application logic.
- Three-thread Agent Architecture: A three-thread agent architecture that temporally decouples data ingestion, model training, and real-time inference, enabling continuous online learning from live data streams.
- Configuration-driven Deployment Model: This allows domain experts to operate ML pipelines without software engineering expertise.
SMOCS is facility platform-agnostic, fault-isolated by design, and horizontally scalable through Docker containerization. The framework is publicly available as open-source software on the Jefferson Lab Github.
Blogger's Review: The SMOCS framework effectively addresses the deployment challenges of machine learning models in complex environments. By leveraging Kafka's capabilities and a containerized approach, it simplifies monitoring and optimization processes, significantly enhancing flexibility and efficiency in industry applications. Its open-source nature also provides a foundation for further research and practice.