Generative AI workflows typically involve provisioning compute, storage, and networking resources to train or fine-tune models and then deploying them for inference. Terraform enables us to define these resources as code, allowing for automated and consistent infrastructure management across various environments.
In this Cloud Lab, you’ll learn how to deploy the complete CI/CD pipeline for training a machine learning model using Amazon SageMaker. You’ll start by setting up an S3 bucket to store the training dataset and model artifacts. Next, you’ll set up a SageMaker environment to use SageMaker Studio and create a SageMaker Project. Moving on, you’ll modify the SageMaker Project according to the use case and deploy the SageMaker endpoint. Finally, you’ll create a Lambda function to test the SageMaker endpoint for real-time inference.
By the end of this Cloud Lab, you will have a firm grasp of the concept of MLOps environment in SageMaker. Also, you’ll be able to create an end-to-end pipeline for your machine learning projects using SageMaker. The architecture diagram below shows the provisioned infrastructure:
