AWS Certified Cloud Practitioner

Explanation

SageMaker Pipelines is the correct answer because it is specifically designed to automate and orchestrate end-to-end machine learning workflows using CI/CD (Continuous Integration/Continuous Deployment) principles.

Key Features of SageMaker Pipelines:

1. Workflow Automation: Creates, manages, and orchestrates ML workflows from data preparation to model deployment

2. CI/CD Integration: Supports continuous integration and deployment of ML models

3. Pipeline Orchestration: Automates the entire ML lifecycle including data preprocessing, training, evaluation, and deployment

4. Version Control: Tracks different versions of pipelines, models, and data

5. Reusability: Allows reuse of pipeline components across different projects

Why other options are incorrect:

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

• C) SageMaker JumpStart: This provides pre-built solutions and models for quick deployment, not full CI/CD pipeline automation

• D) SageMaker Canvas: This is a no-code visual interface for building ML models, not for CI/CD pipeline automation

Use Case Example:

A company can use SageMaker Pipelines to:

• Automatically retrain models when new data arrives

• Deploy new model versions to production

• Run automated testing and validation

• Maintain audit trails of model changes

This aligns perfectly with the requirement to automate the entire ML workflow using CI/CD principles.