Victor Sonck, a Developer Advocate for ClearML, discusses how to effectively monitor machine learning models in production using Grafana integrated with ClearML Serving, a module built on NVIDIA Triton Inference Server. ClearML, an open-source MLOps platform, aids in model deployment and management by offering tools for seamless integration into the MLOps stack. Grafana, along with Prometheus, is utilized to monitor model predictions and performance, enabling users to gain insights, detect model drift, and receive alerts for abnormal model behavior. ClearML Serving facilitates model deployment by connecting inference servers to model repositories, supporting canary deployments, and automating logging and deployment processes. It captures serving metrics and sends them through Apache Kafka to a custom statistics service, which processes and reports them to Prometheus, allowing Grafana to visualize and analyze the results. The integration supports the creation of custom alerts within Grafana, notifying users of issues such as latency or data drift, thus allowing immediate intervention to maintain model accuracy.