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

• URL: http://arxiv.org/abs/2607.02731v1
• Date: Thu, 02 Jul 2026 19:43:19 GMT
• Title: SMOCS: A Streaming Framework for Simplified Deployment, Monitoring, and Optimization of ML Systems in Production
• Abstract summary: Machine learning has demonstrated significant potential for real-time monitoring, optimization, and control of scientific facilities.<n> deploying and maintaining ML models in operational environments remains a substantial engineering challenge.<n>We present SMOCS, a Kafka-based containerized framework that addresses this challenge through three contributions.
• Score: 0.7537220883022466
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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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