In this chapter, you learned about model artifacts, what they contain, and how to use them to export models outside of SageMaker. You also learned how to import and deploy existing models, as well as how to manage endpoints in detail, both with the SageMaker SDK and the
Then, we discussed alternative deployment scenarios with SageMaker, using either batch transform or inference pipelines, as well as outside of SageMaker with container services.
Finally, you learned how to use SageMaker Model Monitor to capture endpoint data and monitor data quality.
In the next chapter, we'll discuss automating machine learning workflows with three different AWS services: AWS CloudFormation, AWS CDK, and AWS Step Functions.