AWS Certified Machine Learning Engineer – Associate (MLA-C01) — Question 186

A company runs its ML workflows on an on-premises Kubernetes cluster. The ML workflows include ML services that perform training and inferences for ML models. Each ML service runs from its own standalone Docker image.

The company needs to perform a lift and shift from the on-premises Kubernetes cluster to an Amazon Elastic Kubernetes Service (Amazon EKS) cluster.

Which solution will meet this requirement with the LEAST operational overhead?

Answer options

• A. Redesign the ML services to be configured in Kubeflow. Deploy the new Kubeflow managed ML services to the EKS cluster.
• B. Upload the Docker images to an Amazon Elastic Container Registry (Amazon ECR) repository. Configure a deployment pipeline to deploy the images to the EKS cluster.
• C. Migrate the training data to an Amazon Redshift cluster. Retrain the models from the migrated training data by using Amazon Redshift ML. Deploy the retrained models to the EKS cluster.
• D. Configure an Amazon SageMaker AI notebook. Retrain the models with the same code. Deploy the retrained models to the EKS cluster.

Correct answer: B

Explanation

Option B is correct because it allows for a straightforward migration of existing Docker images to Amazon EKS without requiring significant changes to the current ML services. The other options involve more complex processes, such as redesigning services (A), migrating data and retraining models (C), or setting up a new environment for model training (D), which introduce additional operational overhead.