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.

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.