Part 2: Observing and scaling MLOps infrastructure on Amazon EKS

This article provides a comprehensive guide to observing and scaling MLOps infrastructure on Amazon EKS, focusing on monitoring strategies for machine learning workloads.

• ML workloads require specialized monitoring beyond traditional application metrics
• Modern ML requires accelerated computing: NVIDIA GPUs, AWS Trainium2, AWS Inferentia2
• Essential metrics include GPU utilization, memory bandwidth, temperature, and ML-specific indicators
• Prometheus collects metrics; Grafana visualizes; Kubernetes HPA scales based on custom metrics
• kube-prometheus-stack provides Prometheus Operator, Node Exporter, kube-state-metrics, Grafana
• ServiceMonitor and PodMonitor enable dynamic Prometheus scrape target configuration
• Third-party observability solutions offer GPU monitoring and ML framework integration
• Layered monitoring approach: infrastructure metrics, application metrics, ML-specific metrics

Organizations should implement comprehensive monitoring combining AWS services, open source tools, and specialized MLOps platforms to optimize resource usage, maintain availability, and scale effectively.