With machine learning at the core of detection systems, the ability to systemically capture, store and analyze the outputs the model generates has become critical for operational efficacy and model governance. This thesis presents a MLOPS pipeline, the Analytics Ingestion System (AIS), developed to collect and persist rich analytics from the Machine Learning powered cybersecurity workflows within the Sysdig ecosystem. AIS offers a model-driven approach to managing inferences, so that any prediction, batch or real-time, is logged, contextualized, so that it can be used for further analysis. The platform has also been designed for scalable storage, message brokers and orchestration tools to allow for modular, cloud native deployments.