Chapter 11: Operationalize ML Projects with SageMaker Projects, Pipelines, and Model Registry
Data scientists used to spend too much time and effort maintaining and manually managing ML pipelines, a process that starts with data, processing, training, and evaluation and ends with model hosting with ongoing maintenance. SageMaker Studio provides features that aim to streamline these operations with continuous integration and continuous delivery (CI/CD) best practices. You will learn how to implement SageMaker projects, Pipelines, and the model registry to help operationalize the ML lifecycle with CI/CD.
In this chapter, we will be learning about the following:
- Understanding ML operations and CI/CD
- Creating a SageMaker project
- Orchestrating an ML pipeline with SageMaker Pipelines
- Running CI/CD in SageMaker Studio
