Exam AWS Certified Machine Learning Engineer - Associate MLA-C01 topic 1 question 82 discussion

SageMaker Pipelines provides a robust way to manage and visualize ML workflows as directed acyclic graphs (DAGs), while SageMaker Experiments helps track and manage the history of model experiments and supports model governance

https://docs.aws.amazon.com/sagemaker/latest/dg/define-pipeline.html I don't see how you can manage the "entire ML flow" (as question asks) with something else other than Studio.

🔑 Keyword: Fine-grained control, DAG visualization, model governance, auditing ✅ Correct Answer: C. Use SageMaker Pipelines and its integration with SageMaker Studio to manage the entire ML workflows. Use SageMaker ML Lineage Tracking for the running history of experiments and for auditing and compliance verifications. Why? SageMaker Pipelines provides workflow orchestration with DAG visualization. SageMaker ML Lineage Tracking enables experiment history and model governance for auditing. Why Others Are Wrong? ❌ A & B. AWS CodePipeline is more suited for software CI/CD than for ML workflows. ❌ D. SageMaker Experiments focuses on tracking experiments but lacks full model lineage tracking.

Contrary to its name, Amazon SageMaker ML Lineage Tracking does: 1. Keep a running history of model discovery experiments. 2. Establish model governance by tracking model lineage artifacts for auditing and compliance verification. Reference: https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html

SageMaker Pipelines handles the orchestration of workflows with fine-grained control. SageMaker Experiments provides the necessary tracking, organization, and governance features for experiments and compliance.

ML Lineage for Audit/Compliance, Studio for DAG, SM Pipeline for entire workflow

The correct answer is C. Options B and D suggest using SageMaker Experiments, which is good for tracking experiments but doesn't provide the comprehensive lineage tracking and governance features that ML Lineage Tracking offers. Running history of experiments: SageMaker ML Lineage Tracking provides a comprehensive way to track the lineage of ML workflows, including datasets, algorithms, hyperparameters, and models. This fulfills the requirement for keeping a running history of model discovery experiments. Model governance for auditing and compliance: ML Lineage Tracking also supports model governance by providing detailed information about each step in the ML process, which is crucial for auditing and compliance verifications.

SageMaker Experiments https://docs.aws.amazon.com/sagemaker/latest/dg/experiments.html

SageMaker ML Lineage Tracking 提供模型和数据的血缘追踪功能，支持审计和合规性。

https://docs.aws.amazon.com/sagemaker/latest/dg/lineage-tracking.html With SageMaker AI Lineage Tracking data scientists and model builders to Keep a running history of model discovery experiments. Establish model governance by tracking model lineage artifacts for auditing and compliance verification.