CI/CD Pipelines and Deployment Strategies

With infrastructure-as-code templates from CloudFormation or CDK defining your ML infrastructure, the natural next step is automating how code changes flow from a repository into production. This is where CI/CD (Continuous Integration/Continuous Deployment)A software engineering practice in which code changes are automatically integrated, tested, and deployed through a repeatable, automated pipeline rather than through manual steps. enters the ML life cycle. For the AWS Certified Machine Learning Engineer – Associate exam, understanding how CI/CD services orchestrate model retraining, updated preprocessing logic, and endpoint configuration changes is essential.

ML workflows benefit from automated pipelines in ways that ...