Amazon SageMaker AI Async Inference now supports inline request payloads
📰 AWS Machine Learning
Learn how to use Amazon SageMaker AI Async Inference with inline request payloads to simplify your ML workflow
Action Steps
- Use the InvokeEndpointAsync API to send inference payloads directly in the request body
- Configure your Amazon SageMaker endpoint to support inline payloads
- Test your async inference workflow with inline payloads to ensure seamless integration
- Compare the performance of inline payloads with traditional S3 upload methods
- Apply inline payload support to your existing ML workflows to simplify and optimize them
Who Needs to Know This
Data scientists and machine learning engineers can benefit from this feature to streamline their inference workflow, reducing the need for additional storage and upload steps
Key Insight
💡 Inline payload support in Amazon SageMaker AI Async Inference eliminates the need for S3 uploads, reducing latency and complexity
Share This
🚀 Simplify your ML workflow with Amazon SageMaker AI Async Inference inline payloads! 📈
Key Takeaways
Learn how to use Amazon SageMaker AI Async Inference with inline request payloads to simplify your ML workflow
Full Article
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.
DeepCamp AI