SageMaker deployment options

Amazon SageMaker offers diverse deployment options to deploy ML models effectively. In this section, you will explore different ways of deploying models using SageMaker, providing technology solutions with scenarios and examples.

Real-time endpoint deployment

In this scenario, you have a trained image classification model, and you want to deploy it to provide real-time predictions for incoming images.

Solution

Create a SageMaker model and deploy it to a real-time endpoint.

Steps

1. Train your model using SageMaker training jobs.
2. Create a SageMaker model from the trained model artifacts.
3. Deploy the model to a real-time endpoint.

Example code snippet

from sagemaker import get_execution_role

from sagemaker.model import Model

from sagemaker.predictor import RealTimePredictor

role = get_execution_role()

model_artifact='s3://your-s3-bucket/path/to/model.tar.gz'

model = Model(model_data=model_artifact...