After data scientists carefully come up with a satisfying machine learning (ML) model, the model must be deployed to be easily accessible for inference by other members of the organization. However, deploying models at scale with optimized cost and compute efficiencies can be a daunting and cumbersome task. Amazon SageMaker endpoints provide an easily scalable and cost-optimized solution for model deployment. The YOLOv5 model, distributed under the GPLv3 license, is a popular object detection model known for its runtime efficiency as well as detection accuracy. In this post, we demonstrate how to host a pre-trained YOLOv5 model on SageMaker endpoints and use AWS Lambda functions to invoke these endpoints.
Solution overview
The following image outlines the AWS services used to host the YOLOv5 model using a SageMaker endpoint and invoke the endpoint using Lambda. The SageMaker notebook accesses a YOLOv5 PyTorch model from an Amazon Simple Storage Service (Amazon S3)

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