MLA-C01 Question 323
Select 4You are a Machine Learning Engineer at a company that deploys models on AWS. You need to create a CI/CD pipeline to automate the build, test, and deployment process of your machine learning model. The pipeline should enable automatic triggering upon code changes in a repository and ensure the model is deployed to Amazon SageMaker. Which combination of AWS services should you use to implement this pipeline?
- A
AWS CodePipeline
- B
AWS CodeCommit
- C
AWS Lambda
- D
Amazon SageMaker
- E
AWS CodeBuild
Show answer and explanation
Correct answers: A, B, D, E
Explanation
To set up an automated CI/CD pipeline for machine learning on AWS, you need AWS CodePipeline to orchestrate the process, AWS CodeCommit to store and track code changes, AWS CodeBuild to build and test the code, and Amazon SageMaker to deploy the trained model. While AWS Lambda can be used in some advanced workflows, it is not necessary for this specific use case.
- A. Correct.
AWS CodePipeline is a key service for orchestrating CI/CD pipelines. It integrates with other services to automate the build, test, and deployment stages.
- B. Correct.
AWS CodeCommit is a source control service where you can host the machine learning model's code repository to trigger the pipeline upon changes.
- C. Incorrect.
AWS Lambda is not necessary for a basic CI/CD pipeline in this scenario, as the pipeline can directly integrate with SageMaker without needing Lambda functions.
- D. Correct.
Amazon SageMaker is essential for deploying machine learning models as endpoints in this scenario. It allows smooth integration with CI/CD pipelines for model deployment.
- E. Correct.
AWS CodeBuild is required for building and testing the machine learning model code and packaging it for deployment.