ML Model Deployment and Testing Patterns on AWS

Deploying machine learning models to production can be risky without the right strategy. Transitioning from a trained model to a live, reliable service requires careful planning, testing, and continuous monitoring.\n\nThis text-based course guides you through the foundational concepts and practical strategies for safe ML model deployment on AWS. You will learn how to minimize downtime, reduce deployment risk, and ensure your models perform reliably under real-world traffic.\n\nWhat you'll learn:\n- Understand foundational MLOps concepts and deployment terminology before diving into cloud services\n- Configure SageMaker endpoints for multi-variant deployments to test models in production safely\n- Implement shadow testing patterns to evaluate new model versions against live traffic without impacting users\n- Apply blue/green and canary deployment strategies to gradually roll out updates and minimize risk\n- Monitor model performance and system metrics to detect drift and operational anomalies early\n- Integrate basic CI/CD principles and observability practices into your machine learning lifecycle\n\nYou will start with core deployment concepts and terminology, then progress through written explanations on configuring AWS SageMaker endpoints, managing traffic routing, and establishing basic observability.\n\nThis course is designed for software engineers, data scientists, and aspiring MLOps practitioners who are new to cloud-based ML deployments. No advanced AWS experience is required to begin.\n\nStart reading today to master the patterns that keep production machine learning systems safe and resilient.