Step 1:

The company commits code changes or new training datasets to a Git repository.

This is the trigger point. A source code or data change initiates the CI/CD pipeline.

Step 2:

CodePipeline detects code changes and starts to build automatically.

CodePipeline monitors the Git repository (for example, AWS CodeCommit, GitHub, or Bitbucket) and automatically triggers the pipeline when changes are detected.

Step 3:

The company builds and deploys ML models and applications to staging servers for testing.

The pipeline runs build, training, and test stages (often using AWS CodeBuild and SageMaker) and deploys artifacts to a staging or test environment for validation.

Step 4:

Human approval is provided after testing is successful.

A manual approval action is a best practice for ML workflows to ensure governance, compliance, and quality checks before production deployment.

Step 5:

CodePipeline deploys ML models and applications to production.

After approval, the pipeline automatically deploys the validated model or application to the production environment.