ObServML: Deployable Python application for compact and modular systems monitoring

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.