ML Monitoring, Maintenance, and Security on AWS

Deploying a machine learning model is only the beginning of its lifecycle; keeping it accurate, secure, and compliant in production requires continuous vigilance. This text-based course guides you through the essential practices of monitoring and securing ML solutions on AWS. You will transition from simply deploying models to actively managing their health, ensuring they remain performant and secure against modern threats. What you will learn: Understand foundational ML security concepts, data privacy, and the AWS shared responsibility model; Monitor model performance and detect data and concept drift using SageMaker Model Monitor; Configure automated alerts and logging with CloudWatch to catch operational issues early; Apply security best practices, including IAM roles, encryption at rest and in transit, and network isolation; Implement governance and compliance strategies for machine learning pipelines; Practice troubleshooting common production issues through detailed written scenarios and architectural walkthroughs. The course begins with essential terminology and security foundations before moving into practical monitoring setups, drift detection, and automated maintenance workflows. Designed for beginners in MLOps, cloud practitioners, and data scientists looking to operationalize their models, this course requires no prior production monitoring experience. Start reading today to build resilient and secure machine learning systems on AWS.