Amazon SageMaker: Machine Learning Workflows

In this lesson, you’re going to learn about Machine Learning Operations with Amazon SageMaker's fully managed workflow service.

Learning Objectives

By the end of this lesson, you will have an understanding of Amazon SageMaker workflow technologies including: 

• MLOps and its advantages, specifically how it integrates with  Amazon SageMaker. 

• Amazon SageMaker Studio, highlighting the functionality of its Integrated Development Environment in utilizing key features of Amazon SageMaker. 

• The roles of SageMaker Debugger and Model Monitor in improving the management of the machine learning lifecycle. 

• The functionality of Amazon SageMaker Projects in assisting organizations to develop CI/CD practices for MLOps engineers. 

• A range of tools for building and managing machine learning pipelines, including SageMaker Model Building Pipelines, SageMaker Operators for Kubernetes, and SageMaker Components for Kubeflow Pipelines. 

Intended Audience

• Data Scientists and Machine Learning engineers, specifically those who are interested in MLOps and Amazon SageMaker. 

Prerequisites

• Have an understanding of MLOps, Amazon SageMaker, and familiarity with Machine Learning terms.

• For more information on these services please see our existing content here: 

  • What is ML Ops? - Using Amazon SageMaker ML Lineage Tracking within your ML Workflow Course

  • Amazon SageMaker - Working With Generative AI on AWS Course

  • Amazon SageMaker Canvas: No-code Machine Learning for Business Analysts Course