Deploying models at scale can be a cumbersome task for many data scientists and machine learning engineers. However, Amazon SageMaker endpoints provide a simple solution for deploying and scaling your machine learning (ML) model inferences. Our last blog post and GitHub repo on hosting a YOLOv5 TensorFlowModel on Amazon SageMaker Endpoints sparked a lot of interest from our readers. Many readers were also interested in learning how to host the YOLOv5 model using PyTorch. To address this issue and with the recent release of the YOLOv8 model from Ultralytics, we present this post on how to host a YOLOv8 PyTorchModel on SageMaker endpoints. The YOLOv8 model, distributed under the GNU GPL3 license, is a popular object detection model known for its runtime efficiency as well as detection accuracy. Amazon SageMaker endpoints provide an easily scalable and cost-optimized solution for model deployment.
Solution overview
The following image outlines the AWS services used to

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