MLOps on Sagemaker

Building an end-to-end ML Pipeline on AWS Sagemaker.

Setup

Install uv before running.

• If you are using AWS SSO, you can activate your profile by running the following command:

  # AWS_PROFILE=sandbox
  aws configure sso --profile sandbox
  # OR
  aws sso login --profile sandbox

• Setup Infra

  # Bootstrap the CDK environment
  ./run cdk-bootstrap
  
  # Deploy the CDK stack
  # This will create the S3 bucket and SageMaker domain
  ./run cdk-deploy

• Create a .env file based on the env.example file and fill in the required values.

• Clean Up

  # Destroy infra
  ./run cdk-destroy
  
  # Clean up local files
  ./run clean

Usage

• Run the Pipeline:

  # to run the pipeline with SageMaker
  ./run pipeline
  
  # to run the pipeline locally
  ./run pipeline --local

  

• Test the endpoint:

  AWS_PROFILE=sandbox uv run scripts/test_endpoint.py

  Single penguin data:
  {
      "island": "Torgersen",
      "culmen_length_mm": 39.1,
      "culmen_depth_mm": 18.7,
      "flipper_length_mm": 181,
      "body_mass_g": 3750
  }
  
  Response status: 200
  Result: {
      'prediction': 'Adelie',
      'confidence': 0.5893437266349792,
      'probabilities': {
          'Adelie': 0.5893437266349792,
          'Chinstrap': 0.16670739650726318,
          'Gentoo': 0.2439488023519516
      }
  }

  

Contributing

If you find any problems with the code or have any ideas on improving it, please open an issue and share your recommendations.