MLOps Foundations with AWS SageMaker and Azure ML

Transitioning machine learning models from a local notebook to a reliable production environment is one of the biggest challenges in AI today. This text-based course guides you through the foundational concepts of Machine Learning Operations (MLOps) using AWS SageMaker and Azure ML. You will gain the skills to build, deploy, and monitor scalable machine learning pipelines in the cloud. By reading through clear explanations and structured written exercises, you will understand how to automate workflows, manage model registries, and maintain model performance over time. What you'll learn: - Understand core MLOps terminology, lifecycle phases, and foundational cloud concepts. - Configure and manage training jobs using AWS SageMaker. - Build and orchestrate machine learning pipelines in Azure ML. - Deploy trained models to production endpoints for real-time and batch predictions. - Monitor model performance and set up alerts for data drift and concept drift. - Apply basic CI/CD principles to automate model retraining and deployment. The course begins with essential definitions and MLOps principles before guiding you step-by-step through practical cloud workflows. You will read comprehensive walk-throughs of both AWS SageMaker and Azure ML, learning how to leverage their unique features for end-to-end model management. This course is designed for beginners, aspiring data scientists, and software engineers looking to enter the field of MLOps. No prior experience with AWS or Azure is required, though a basic familiarity with machine learning concepts is helpful. Start reading today to bridge the gap between machine learning code and production-ready cloud systems.