Monitoring Data Quality Issues with Amazon SageMaker

Amazon SageMaker is a service that enables you to build, train, and deploy machine learning models in the public AWS cloud. In addition, SageMaker provides a model monitoring feature that allows you to monitor the quality of your deployed models over time.

Learning how to use the model monitoring feature in Amazon SageMaker will benefit anyone who is looking to deploy machine learning models in production environments.

In this hands-on lab, you will use a Jupyter notebook to examine a dataset, an endpoint, and configure a model monitor schedule for the endpoint.

Please note: This lab uses an Amazon SageMaker notebook and endpoint, which can take up to ten minutes to deploy. Please ensure you have enough time available before starting the lab.

Learning objectives

Upon completion of this beginner-level lab, you will be able to:

• Access a JupyterLab notebook
• Generate and examine a synthetic dataset
• Examine a deployed model endpoint
• Configure a model monitor schedule for the endpoint

Intended audience

• Candidates for the AWS Certified Machine Learning Engineer Associate certification
• Cloud Architects
• Data Engineers
• DevOps Engineers
• Machine Learning Engineers

Prerequisites

Familiarity with the following will be beneficial but is not required:

• Amazon SageMaker
• Amazon S3
• The Python programming language

The following content can be used to fulfill the prerequisites:

• Amazon SageMaker: Machine Learning Workflows
• Introduction to Amazon S3
• Python for Beginners