Training and deploying models with built-in algorithms
Amazon SageMaker lets you train and deploy models in many different configurations. Although it encourages best practices, it is a modular service that lets you do things your own way.
In this section, we'll first look at a typical end-to-end workflow, where we use SageMaker from data upload all the way to model deployment. Then, we'll discuss alternative workflows, and how you can cherry-pick the features that you need. Finally, we will take a look under the hood, and see what happens from an infrastructure perspective when we train and deploy.
Understanding the end-to-end workflow
Let's look at a typical SageMaker workflow. You'll see it again and again in our examples, as well as in the AWS notebooks available on GitHub (https://github.com/awslabs/amazon-sagemaker-examples/):
- Make your dataset available in Amazon S3: In most examples, we'll download a dataset from the internet, or load...
