AWS customers are relying on Infrastructure as Code (IaC) to design, develop, and manage their cloud infrastructure. IaC ensures that customer infrastructure and services are consistent, scalable, and reproducible, while being able to follow best practices in the area of development operations (DevOps).
One possible approach to manage AWS infrastructure and services with IaC is Terraform, which allows developers to organize their infrastructure in reusable code modules. This aspect is increasingly gaining importance in the area of machine learning (ML). Developing and managing ML pipelines, including training and inference with Terraform as IaC, lets you easily scale for multiple ML use cases or Regions without having to develop the infrastructure from scratch. Furthermore, it provides consistency for the infrastructure (for example, instance type and size) for training and inference across different implementations of the ML pipeline. This lets you route requests and incoming traffic to different Amazon SageMaker endpoints.

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