Running CI/CD in SageMaker Studio
The ML pipeline we've seen running previously is just one part of our CI/CD system at work. The ML pipeline is triggered by a CI/CD pipeline in AWS CodePipeline. Let's dive into the three CI/CD pipelines that the SageMaker project template sets up for us.
There are three CodePipeline pipelines:
<project-name-prefix>-modelbuild: The purpose of this pipeline is to run the ML pipeline and create an ML model in SageMaker Model Registry. This CI/CD pipeline runs the ML pipeline as a build step when triggered by a commit to the repository. The ML model in the SageMaker model registry needs to be approved in order to trigger the next pipeline,modeldeploy.<project-name-prefix>-modeldeploy: The purpose of this pipeline is to deploy the latest approved ML model in the SageMaker model registry as a SageMaker endpoint. The build process deploys a staging endpoint first and requests manual approval before proceeding to deploy...
