Model Monitoring and Drift Detection
Explore how to maintain production ML model health using Amazon SageMaker Model Monitor. Understand key concepts like data quality, model quality, bias drift, and feature attribution drift. Learn to establish baselines for drift detection, configure monitoring schedules with data capture, and set up automated alerts to ensure reliable and accurate ML deployments in AWS environments.
Production ML models do not operate in a vacuum. Once deployed, they face shifting data distributions, evolving user behavior, and upstream pipeline changes that can silently erode prediction quality. For the AWS Certified Machine Learning Engineer – Associate exam, understanding how to detect and respond to this degradation is essential. This lesson focuses on the deployment and monitoring stage of the ML life cycle, where Amazon SageMaker Model Monitor serves as the primary mechanism for continuous, ML-specific observability of deployed endpoints.
Model drift refers to the divergence between the statistical assumptions a model learned during training and the characteristics of real-world inference data that it encounters in production. A model trained on last quarter's customer data may perform well initially, but as purchasing patterns shift, its predictions quietly degrade. SageMaker Model Monitor addresses this by automating the comparison of live inference data against training-time baselines and surfacing violations before they impact business outcomes.
A critical distinction for the exam is between SageMaker Model Monitor and Amazon CloudWatch. CloudWatch tracks infrastructure-level metrics such as CPU utilization, memory consumption, endpoint invocation latency, and HTTP error rates. Model Monitor, by contrast, is purpose-built for ML metrics, including data quality violations, prediction accuracy degradation, bias drift, and feature attribution shifts. CloudWatch tells you whether your endpoint is healthy; Model Monitor tells you whether your model is healthy.
The goal of this lesson is to walk through how Model Monitor detects drift, how baselines and constraints are established, and how monitoring schedules automate the process in production.
Types of drift in ML models
SageMaker Model ...