In recent years, MLOps has emerged to bring DevOps processes to the machine learning (ML) development process, aiming at more automation in the execution of repetitive tasks and at smoother interoperability between tools. Among the different stages in the ML lifecycle, model monitoring involves the continuous supervision of the model performance over time, involving the combination of techniques in four categories: outlier detection, data drift detection, explainability and adversarial attacks.

Nowadays, most of the available model monitoring tools follow a scheduled batch processing approach or analyse model performance using isolated subsets of the inference data. However, for the continuous monitoring of models, stream processing platforms show several advantages, including support for continuous data analytics, scalable processing of large amounts of data and first-class support for window-based aggregations useful for concept drift detection.