SMOCS: A Streaming Framework for Simplified Deployment, Monitoring, and Optimization of ML Systems in Production
arXiv:2607.02731v1 Announce Type: cross 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 addresse
The increasing maturity of AI and ML models for real-time applications necessitates robust, standardized deployment frameworks to overcome current integration complexities.
This framework addresses a significant bottleneck in operationalizing ML, making scientific and industrial AI applications more accessible and scalable by simplifying deployment and maintenance.
Deployment and maintenance of ML models in complex, data-rich operational environments become streamlined, reducing custom engineering efforts for each new application.
- · Scientific facilities
- · ML engineers
- · Industrial IoT sectors
- · AI/ML platform providers
- · Integrators relying on bespoke solutions
- · Teams without standardized MLOps practices
Faster and more reliable integration of ML into real-world systems, accelerating AI adoption in critical infrastructure.
Increased efficiency and precision in monitoring, optimization, and control across various scientific and industrial domains.
Potential for new AI-driven services and products as the barrier to entry for operationalizing ML is significantly lowered.
This signal links to a primary source. Continuum Brief monitors and indexes it as part of the live intelligence stream — we do not republish source content.
Read at arXiv cs.AI