Model Deployment
In the previous chapter, you explored various aspects of Amazon SageMaker, including different instances, data preparation in Jupyter Notebook, model training with built-in algorithms, and crafting custom code for training and inference. Now, your focus shifts to diverse model deployment choices using AWS services.
If you are navigating the landscape of model deployment on AWS, understanding the options is crucial. One standout service is Amazon SageMaker – a fully managed solution that streamlines the entire machine learning (ML) life cycle, especially when it comes to deploying models. There are several factors that influence the model deployment options. As you go ahead in this chapter you will learn different options to deploy models using SageMaker.
