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

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.

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.