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arXiv · 2607.02731

SMOCS: A Streaming Framework for Simplified Deployment, Monitoring, and Optimization of ML Systems in Production

Armen Kasparian·Kishansingh Rajput·Malachi Schram·John Vennekate
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Abstract

Machine learning has demonstrated 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: 1) a layered abstraction over Apache Kafka that separates infrastructure from application logic, 2) a three-thread agent architecture that temporally decouples data ingestion, model training, and real-time inference enabling continuous online learning from live data streams, and 3) a configuration-driven deployment model that enables 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.

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Armen Kasparian, Kishansingh Rajput, Malachi Schram, John Vennekate. 2026-07-02. SMOCS: A Streaming Framework for Simplified Deployment, Monitoring, and Optimization of ML Systems in Production. https://arxiv.org/abs/2607.02731

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