Answer: SageMaker Pipelines

## 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.

Author: Ritesh Yadav