AWS Certified Cloud Practitioner

Explanation

SageMaker Pipelines is the correct answer because it is specifically designed to automate and orchestrate the entire machine learning workflow as a CI/CD pipeline.

Key Features of SageMaker Pipelines:

• End-to-end automation: Supports the complete ML workflow from data preparation, feature engineering, model training, model evaluation, to model deployment

• CI/CD integration: Can be integrated with CI/CD tools like AWS CodePipeline for automated model building and deployment

• Workflow orchestration: Provides a way to define, visualize, and execute ML workflows with dependencies between steps

• Reproducibility: Ensures consistent and reproducible ML workflows

Why other options are incorrect:

• A) SageMaker Ground Truth: Used for data labeling and annotation, not workflow automation

• C) SageMaker JumpStart: Provides pre-built solutions and model templates for quick ML project setup

• D) SageMaker Canvas: A no-code visual interface for business analysts to build ML models

SageMaker Pipelines enables organizations to implement MLOps practices by automating the complete ML lifecycle through reusable, scalable, and maintainable workflows.