AI Infrastructure and Tools for Model Deployment

Transitioning a machine learning model from a local notebook to a reliable production environment requires robust infrastructure. This course guides you through the core tools and frameworks used to manage, deploy, and monitor AI products at scale. You will understand how to design and maintain machine learning pipelines, ensuring your models remain accurate, accessible, and performant in real-world applications. In this course, you will: 1. Learn the foundational concepts of MLOps and the lifecycle of AI products. 2. Configure and track experiments using MLflow to manage model versioning. 3. Understand pipeline orchestration and data validation using TFX. 4. Explore enterprise-scale model management and deployment patterns with SageMaker and Michelangelo. 5. Implement modern observability and monitoring strategies to detect model and data drift. We start with essential terminology and the foundations of model management before diving into step-by-step written walkthroughs of deployment workflows, pipeline automation, and production monitoring. This course is designed for beginners, including aspiring ML engineers, data scientists, and product managers, with no prior infrastructure experience required. Read through the structured explanations and practical scenarios to start building reliable AI infrastructure today.