Using Amazon SageMaker ML Lineage Tracking within your ML Workflow

1m 57s

In this lesson, we are going to focus on the ML lineage tracking of SageMaker, to help you create and store steps of your machine-learning workflow.

Learning Objectives

  • Learn what ML Ops is at a high level
  • Understand how SageMaker lineage tracking can form a part of your ML workflow
  • Discover the different types of entities that can be captured
  • Explain what a model lineage graph is

Intended Audience

  • Data scientists and ML engineers, specifically those in a role relating to ML Operations
  • Anyone looking to take AWS certifications with a domain focus on data engineering and machine learning


  • Have an understanding of Amazon SageMaker and be familiar with Machine Learning terms
About the Author
Learning paths

Stuart has been working within the IT industry for two decades covering a huge range of topic areas and technologies, from data center and network infrastructure design, to cloud architecture and implementation.

To date, Stuart has created 250+ courses relating to cloud computing reaching over 1 million+ students.

Stuart is a member of the AWS Community Builders Program for his contributions towards AWS.

He is AWS certified and accredited in addition to being a published author covering topics across the AWS landscape.

Stuart enjoys writing about cloud technologies and you will find many of his articles within our blog pages.

Covered Topics