What are the best practices for ML Ops monitoring?

What are the best practices for ML Ops monitoring?

Best practices for ML Ops monitoring include setting up real-time monitoring, logging metrics, and alerting on anomalies.

How can I implement real-time monitoring for my ML models?

You can implement real-time monitoring by using tools like Prometheus or Grafana to visualize metrics and set alerts.

What metrics should I track for my ML models?

Key metrics to track include model accuracy, latency, throughput, and resource utilization.

How do I handle model drift in production?

To handle model drift, regularly retrain your models with new data and monitor performance metrics to detect changes.