Answer: SageMaker Pipelines

## 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.

Author: Ritesh Yadav