Monitoring Model Inference With Amazon SageMaker

In this lesson, you will explore how to effectively track and maintain the performance of your deployed machine learning models. You will learn about tools and techniques to ensure your models continue to deliver accurate predictions in real-world scenarios.

Learning Objectives

• Identify core components of effective ML model monitoring.

• Recognize the four main categories of model drift in machine learning systems.

• Describe key features of Amazon SageMaker Model Monitor.

• Explain the capabilities of Amazon SageMaker Model Dashboard.

• Use Amazon CloudWatch to monitor SageMaker resources.

• Deploy and monitor models using Amazon SageMaker Endpoints.

Intended Audience

This lesson is designed for machine learning engineers, data scientists, and DevOps professionals looking to effectively monitor and maintain machine learning models in production environments using Amazon SageMaker and related AWS services.

Prerequisites

To get the most out of this lesson, you should have some basic working knowledge of machine learning concepts, AWS cloud services, and Amazon SageMaker. Experience with deploying ML models is beneficial.