A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only. What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
A) Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
B) 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.
C) Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
D) Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

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The Answer of A Machine Learning Specialist previously trained a logistic regression model using scikit-learn on a local machine, and the Specialist now wants to deploy it to production for inference only. What steps should be taken to ensure Amazon SageMaker can host a model that was trained locally?
A) Build the Docker image with the inference code. Tag the Docker image with the registry hostname and upload it to Amazon ECR.
B) 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.
C) Serialize the trained model so the format is compressed for deployment. Build the image and upload it to Docker Hub.
D) Build the Docker image with the inference code. Configure Docker Hub and upload the image to Amazon ECR.

A Machine Learning Specialist Previously Trained a Logistic Regression Model