Registering models to SageMaker Model Registry

In Chapter 6, SageMaker Training and Debugging Solutions, we used the deploy() method of the Estimator instance to immediately deploy our ML model to an inference endpoint right after using the fit() method to train the model. When performing ML experiments and deployments in production, a model may have to be analyzed and evaluated first before proceeding with the deployment step. The individual or team performing the analysis would review the input configuration parameters, the training data, and the algorithm used to train the model, along with other relevant information available. Once the data science team has to work with multiple models, managing and organizing all of these would be much easier using a model registry.

What’s a model registry? A model registry is simply a repository that focuses on helping data scientists and ML practitioners manage, organize, and catalog ML models. After the training step, the data science...