SREcon23 Asia/Pacific - Observability in the MLOps Lifecycle with Prometheus

Observability in the MLOps Lifecycle with Prometheus

Shivay Lamba

MLOps is widely talked about and used to make the practice of deploying, managing, and monitoring ML models in production easier. Monitoring ML training or evaluation jobs is obviously very important however it is more important to monitor once an ML model is deployed.

This talk first starts by giving a gentle introduction about how ML deployments should be monitored, briefly talking about edge cases in production, data drift, concept drift, …

asia easier evaluation important jobs lifecycle ml models mlops monitor monitoring observability pacific practice prometheus training