How to Deploy YOLOv8 Using Intel's OpenVINO and Amazon SageMaker

The post by Ryan Ball provides a comprehensive guide on deploying a YOLOv8 object detection model in ONNX format to an Amazon SageMaker endpoint using OpenVino as the execution provider. It begins with setting up an Amazon SageMaker Studio domain and user profile, followed by downloading and converting the YOLOv8 model to ONNX format, which is then uploaded to Amazon S3. The process includes building a Docker image on AWS Elastic Container Registry (ECR) for deployment, and finally, deploying the model to a SageMaker endpoint where it is configured to run inference using OpenVINO. The post highlights the steps involved in creating a custom service script and Docker files, building and pushing the Docker image to ECR, and executing the model inference on SageMaker. Additionally, it discusses the cost efficiency of using OpenVINO compared to the default CPU execution provider and concludes by emphasizing the benefits of using Amazon SageMaker for deploying and serving machine learning models.