Enhanced metrics for Amazon SageMaker AI endpoints: deeper visibility for better performance

This article announces enhanced metrics for Amazon SageMaker AI endpoints, providing granular visibility into ML model performance and resource utilization in production environments.

• Instance-level metrics track CPU, GPU, memory across all SageMaker endpoints
• Container-level metrics available for Inference Components hosting multiple models
• Configurable publishing frequency: 60 seconds (default) or 10/30 seconds for critical workloads
• Enable with single parameter: EnableEnhancedMetrics and MetricsPublishFrequencyInSeconds
• Track per-model costs in multi-tenant deployments using GPU allocation metrics
• Monitor real-time GPU utilization and availability across inference components
• Invocation metrics include request patterns, errors, latency at instance/container level
• Create operational dashboards combining cluster-wide and per-model monitoring
• Only utilization metrics incur CloudWatch charges; other metrics published at no cost

Enhanced metrics enable accurate cost attribution, real-time resource monitoring, and efficient troubleshooting for production ML workloads at scale.