Google, IBM & Meta Certificates — All 10,000+ Courses at 40% Off
One annual plan covers every course and certificate on Coursera. 40% off for a limited time.
Get Full Access
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
