ML Observability and Drift Detection with SageMaker

Once a machine learning model is deployed to production, its performance can degrade over time due to changing real-world data. Understanding how to track, detect, and resolve these changes is critical to keeping your AI systems reliable and accurate. This text-only course guides you through the core concepts of ML observability, data drift, concept drift, and model bias. You will learn how to set up automated monitoring pipelines using SageMaker and AWS services, enabling you to detect anomalies early and maintain high-performing machine learning systems in production. What you'll learn: 1. Understand the foundational concepts of ML observability, data quality degradation, and concept drift. 2. Configure SageMaker Model Monitor to automatically track baseline data and detect real-time deviations. 3. Detect model bias and explainability drift using SageMaker Clarify. 4. Build automated alerting and retraining pipelines using AWS integration patterns. 5. Practice diagnosing performance drops through detailed written scenarios and step-by-step text guides. 6. Apply modern MLOps best practices to maintain robust, self-healing machine learning workflows. You will start with the fundamental terminology of model degradation before moving into hands-on configuration of monitoring jobs, bias detection, and automated alerting systems. This course is designed for beginner MLOps engineers, data scientists, and developers who want to transition from building models to monitoring them in production. No prior experience with production monitoring is required, though a basic understanding of machine learning concepts is helpful. Start reading today to build reliable, self-monitoring machine learning systems.