Running machine learning (ML) models in production requires more than just infrastructure resilience and scaling efficiency. You need nearly continuous visibility into performance and resource utilization. When latency increases, invocations fail, or resources become constrained, you need immediate insight to diagnose and resolve issues before they impact your customers.
Until now, Amazon SageMaker AI provided Amazon CloudWatch metrics that offered useful high-level visibility, but these were aggregate metrics across all instances and containers. While helpful for overall health monitoring, these aggregated metrics obscured individual instance and container details, making it difficult to pinpoint bottlenecks, improve resource utilization, or troubleshoot effectively.
SageMaker AI endpoints now support enhanced metrics with configurable publishing frequency. This launch provides the granular visibility needed to monitor, troubleshoot, and improve your…
