Which of the following AWS services can be used for automating the deployment and scaling of machine learning models into production environments?

Question

Options

• AAWS CloudFormation
• BAmazon SageMaker Model Monitor
• CAWS CodePipeline
• DAmazon SageMaker Pipelines

• A
  3% (2)
• B
  2% (1)
• C
  6% (4)
• D
  89% (58)

Explanation

Amazon SageMaker Pipelines is a purpose-built CI/CD orchestration service for ML workflows. It automates the full model lifecycle-data preprocessing, training, evaluation, approval gates, model registration, and deployment to SageMaker Endpoints-with built-in versioning and auditability. Option A (CloudFormation) is general infrastructure automation and does not understand ML-specific steps like model training or evaluation. Option B (SageMaker Model Monitor) monitors deployed models for data drift and quality degradation but does not handle deployment automation. Option C (AWS CodePipeline) is a general-purpose software CI/CD service; while it can trigger SageMaker jobs, it lacks native ML pipeline steps and model registry integration. SageMaker Pipelines is the correct dedicated ML operations automation service.

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