What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
Serialize the trained model so the format is compressed for deployment. Tag the Docker image with the registry hostname and upload it to Amazon S3.
Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.
Explanations:
Building the Docker image with inference code and uploading it to Amazon ECR is correct, as SageMaker can pull custom images from ECR for model deployment.
While serializing the model for deployment is necessary, Amazon S3 is used for model artifacts, not Docker images; ECR is required for Docker images.
Docker Hub is not typically used with SageMaker for production deployment, as SageMaker expects Docker images in Amazon ECR for integration and security.
Configuring Docker Hub is unnecessary; SageMaker requires the Docker image to be in Amazon ECR, not Docker Hub, for streamlined integration and security.
As I understand it, the answer is:
Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.