Lab - Orchestrate a Machine Learning Workflow using Amazon SageMaker Pipelines and SageMaker Model Registry

In this lab, you manage different steps of an automated machine learning (ML) workflow. This includes data loading, data transformation, training and tuning, model evaluation, bias detection, and deployment. You also use the model registry for storing the trained models.

Objectives

• Create a SageMaker pipeline.
• View pipeline steps and artifacts.
• Register trained models with the model registry through a pipeline step.

Prerequisites

• Basic navigation of the AWS Management Console
• Basic familiarity with Machine Learning concepts

Outline

Task 1: Set up the environment

Task 2: Create and monitor a SageMaker pipeline