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Streamline custom environment provisioning for Amazon SageMaker Studio: An automated CI/CD pipeline approach
- Amazon SageMaker Studio's automated continuous integration and delivery (CI/CD) pipeline solution helps deploy custom Docker images to SageMaker Studio domains.
- The solution promoted consistency of analytical environment standards across data science teams across enterprises.
- The pipeline is automated by AWS CodePipeline and automates creation and attachment of Docker images.
- The pipeline automates code base check-out, Docker image creation based on configuration, push to Amazon Elastic Container Registry.
- If no high-security vulnerabilities are found, the deployment proceeds to manual approval stage before deployment.
- The default automation helps create SageMaker domain and attach custom images to the domain.
- The solution is geared towards platform teams and ML engineers responsible for managing and standardizing custom environments across organizations.
- Individual data scientists seeking self-service experience are advised to the native Docker support in SageMaker Studio.
- The authors also explain how to add more custom images using Dockerfile specifications.
- After a successful deployment, the custom image is attached to domain configuration.
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