DOAJ Open Access 2026

ObServML: Deployable Python application for compact and modular systems monitoring

Ádám Ipkovich János Abonyi Alex Kummer

Abstrak

ObservML enables the combination of training and deploying ML monitoring models within a single microservices-based system. Its application focuses on monitoring problems that can be solved with fault detection and isolation (FDI), time series analysis, and process mining through an operator-friendly and adaptable framework based on MLOps practices. The framework is developed to connect to RabbitMQ for real-time data communication and MLflow for model versioning. It supports a wide range of machine learning techniques, including decision trees, autoencoders, and time series models, providing a robust toolkit for anomaly detection and predictive maintenance, and can be extended as required.

Topik & Kata Kunci

Computer software

Penulis (3)

Ádám Ipkovich

János Abonyi

Alex Kummer

Format Sitasi

APA MLA BibTeX

Ipkovich, Á., Abonyi, J., Kummer, A. (2026). ObServML: Deployable Python application for compact and modular systems monitoring. https://doi.org/10.1016/j.softx.2026.102596

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Informasi Jurnal

Tahun Terbit: 2026
Sumber Database: DOAJ
DOI: 10.1016/j.softx.2026.102596
Akses: Open Access ✓