Designing ML Pipelines with SageMaker and CI/CD

Transitioning machine learning models from a local notebook to a reliable, automated production system is one of the greatest challenges in modern software engineering. This text-based course guides you through the process of operationalizing your workflows using SageMaker and continuous integration and continuous delivery (CI/CD) practices. You will transition from manual model training to designing robust, automated pipelines that handle data preprocessing, training, evaluation, and deployment. You will also learn to implement model governance and automated triggers to ensure your models remain accurate and up to date. What you'll learn: - Understand foundational MLOps concepts and the lifecycle of machine learning systems. - Build automated workflows using SageMaker Pipelines for data prep, training, and evaluation. - Configure model registry governance to track, version, and approve your machine learning models. - Implement CI/CD automation to test and deploy pipelines whenever code changes. - Apply modern observability practices to monitor pipeline health and model performance in production. - Design event-driven triggers to automatically retrain models when new data arrives. The course begins with core MLOps terminology and pipeline architecture before moving into the configuration of SageMaker steps, registry management, and automated deployment workflows. You will read through detailed architectural explanations, explore structured code snippets, and practice through written design exercises. This course is designed for software engineers, data scientists, and aspiring MLOps engineers who are new to machine learning automation. No prior DevOps or advanced SageMaker experience is required to get started. Start building reliable, automated machine learning pipelines today.