What protocols should be established for monitoring machine learning models in production?

Establishing robust monitoring protocols for machine learning models in production involves several key components: First, define clear objectives for monitoring, such as performance metrics (accuracy, latency) and resource utilization (CPU, memory). Set up automated alerts for anomalies based on thresholds for these metrics. Implement logging mechanisms to capture input data and model output for auditing. Periodically review model performance against baseline metrics to identify degradation. Utilize tools like Prometheus or Grafana for real-time monitoring and visualization. Lastly, create a feedback loop for continuous improvement based on monitoring insights, involving regular retraining and model evaluation.