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Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch | Amazon Web Services
Amazon SageMaker AI provides 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 AI workloads with detailed observability: Single-model endpoints (SME) and Inference component (IC) endpoints.
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Introducing container caching in Amazon SageMaker AI for faster model scaling | Amazon Web Services
Today, we’re excited to announce container image caching for Amazon SageMaker AI inference, the next major advancement in our faster scaling optimization journey. This speeds up…
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Monitor and debug generative AI inference with SageMaker detailed metrics and Insights dashboard on CloudWatch | Amazon Web Services
Amazon SageMaker AI provides fully managed real-time inference hosting for machine learning models. You deploy a model to a SageMaker endpoint backed by one or more compute…