基本信息
- 来源: blogs_podcasts
- 原始来源: https://aws.amazon.com/blogs/machine-learning/enhanced-metrics-for-amazon-sagemaker-ai-endpoints-deeper-visibility-for-better-performance
来源摘要/节选
公开展示已截断至最多 800 个字符；请访问原始来源查看完整上下文。
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.…
来源说明
当前只保存了公开页面节选，不代表原文全文。请以原始来源为准。
本页只呈现已做哈希绑定的来源证据，不包含基于旧正文或缺失原文的扩展推断。