In the context of MLOps, what does model monitoring involve?

Model monitoring in the context of MLOps primarily focuses on evaluating the performance of machine learning models after they have been deployed. This involves continuously assessing how the model performs in real-world scenarios, ensuring it meets the desired metrics and delivers accurate predictions as expected.

Monitoring is crucial because models can degrade over time due to changes in data, known as concept drift, or other external factors. By systematically evaluating the model's performance post-release, teams can identify issues early, implement necessary adjustments, and maintain the model's effectiveness. This involves setting up various metrics and alerts to track performance, such as accuracy, precision, recall, and other relevant indicators that reflect the model's health and reliability.

While tracking model costs, conducting user training, and compliance auditing are important aspects of the MLOps lifecycle, they do not specifically pertain to the evaluation of a model's performance after deployment, which is at the core of model monitoring.