Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch | Amazon Web Services

Monitoring and troubleshooting generative AI inference endpoints operating at scale is challenging. When your large language model (LLM) endpoint’s P99 latency spikes, you must determine in minutes whether the root cause is GPU memory pressure, a saturated KV cache, unbalanced traffic across Availability Zones, or an auto scaling policy that hasn’t triggered. The shift from training to serving is reshaping how teams deploy LLMs and other generative AI models in production. Machine learning (ML) platform engineers, MLOps teams, and site reliability engineers (SREs) must keep inference endpoints healthy, responsive, and cost-efficient, often across dozens of models and hundreds of GPU instances.

Amazon SageMaker AIprovides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute instances, and SageMaker handles provisioning and scaling. SageMaker supports multiple endpoint architectures. This post focuses on the two most relevant to generative...

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