Amazon SageMaker

Overview

Amazon SageMaker is a fully managed service for building, training, and deploying machine learning models at scale.

Real-life Use Cases

• Cloud Architect: Design end-to-end ML pipelines for production workloads.

• DevOps Engineer: Automate model deployment and monitoring.

Terraform Example

resource "aws_sagemaker_notebook_instance" "ml_notebook" {
  name          = "ml-notebook"
  instance_type = "ml.t2.medium"
  role_arn      = aws_iam_role.sagemaker_execution.arn
}

AWS CLI Example

aws sagemaker create-notebook-instance --notebook-instance-name ml-notebook --instance-type ml.t2.medium --role-arn arn:aws:iam::123456789012:role/SageMakerExecutionRole

Best Practices

• Use lifecycle configurations for automation.

• Monitor model drift and retrain as needed.

Common Pitfalls

• Not securing notebook endpoints.

• Underestimating storage needs for training data.

Joke: Why did the ML model go to SageMaker? To get some training!