Introducing container caching in Amazon SageMaker AI for faster model scaling | Amazon Web Services
Artificial Intelligence
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 end-to-end latency by up to 2x for generative AI models during scale-out events.
Over the years, Amazon SageMaker AI has continued to reduce latency across these scaling stages: detecting the need to scale out, provisioning instances, downloading container images, fetching model weights, and starting containers. Amazon SageMaker AI previously introduced sub-minute Amazon CloudWatch metrics to help detect scale-out needs up to 6x faster than traditional mechanisms and launched an inference component data caching solutionthat stores container images and model artifacts on already running instances. This approach reduced the cold start latency for scaling inference component operations that reuse existing instances. Together, these features improved auto scaling responsiveness for scenarios where an inference component can be placed on an already provisioned instance...
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