Today, we’re announcing inline payload support for Amazon SageMaker AI Async Inference. Customers can now send inference payloads directly in the request body of the InvokeEndpointAsync API, removing the need to upload input data to Amazon Simple Storage Service (Amazon S3) before each invocation.
The continuous evolution of AI inference demands more efficient data handling mechanisms, and inline payloads address a friction point in scalable AI service deployment.
This update streamlines the deployment and operation of AI models on SageMaker, reducing complexity and potential latency for large-scale asynchronous inference tasks.
Customers can now directly embed inference data in API requests, eliminating the prior requirement to upload input data to S3, simplifying the architecture for many AI applications.
- · AWS
- · Developers building AI applications
- · Companies using SageMaker for AI inference
Reduced operational overhead and improved developer experience for SageMaker users.
Faster iteration and deployment cycles for AI models requiring asynchronous inference.
Potentially increased adoption of SageMaker for use cases with dynamic or sensitive input data where S3 intermediaries were impractical.
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Read at AWS Machine Learning Blog